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Breast cancer (BCa) and prostate cancer (PCa) are the most prevalent types of cancers. We aimed to understand and analyze the care pathways for BCa and PCa patients followed at a hospital setting by analyzing their different treatment lines. We evaluated the association between different treatment lines and the lifestyle and demographic characteristics of these patients. Two datasets were created using the electronic health records (EHRs) and information collected through semi-structured one-on-one interviews. Statistical analysis was performed to examine which variable had an impact on the treatment each patient followed. In total, 83 patients participated in the study that ran between January and November 2018 in Beacon Hospital. Results show that chemotherapy cycles indicate if a patient would have other treatments, i.e., patients who have targeted therapy (25/46) have more chemotherapy cycles (95% CI 4.66–9.52, p = 0.012), the same is observed with endocrine therapy (95% CI 4.77–13.59, p = 0.044). Patients who had bisphosphonate (11/46), an indication of bone metastasis, had more chemotherapy cycles (95% CI 5.19–6.60, p = 0.012). PCa patients with tall height (95% CI 176.70–183.85, p = 0.005), heavier (95% CI 85.80–99.57, p< 0.001), and a BMI above 25 (95% CI 1.85–2.62, p = 0.017) had chemotherapy compared to patients who were shorter, lighter and with BMI less than 25. Initial prostate-specific antigen level (PSA level) indicated if a patient would be treated with bisphosphonate or not (95% CI 45.51–96.14, p = 0.002). Lifestyle variables such as diet (95% CI 1.46–1.85, p = 0.016), and exercise (95% CI 1.20–1.96, p = 0.029) indicated that healthier and active BCa patients had undergone surgeries. Our findings show that chemotherapy cycles and lifestyle for BCa, and tallness and weight for PCa may indicate the rest of treatment plan for these patients. Understanding factors that influence care pathways allow a more person-centered care approach and the redesign of care processes.
Ornela Bardhi; Begonya Garcia-Zapirain; Roberto Nuño-Solinis. Factors Influencing Care Pathways for Breast and Prostate Cancer in a Hospital Setting. International Journal of Environmental Research and Public Health 2021, 18, 7913 .
AMA StyleOrnela Bardhi, Begonya Garcia-Zapirain, Roberto Nuño-Solinis. Factors Influencing Care Pathways for Breast and Prostate Cancer in a Hospital Setting. International Journal of Environmental Research and Public Health. 2021; 18 (15):7913.
Chicago/Turabian StyleOrnela Bardhi; Begonya Garcia-Zapirain; Roberto Nuño-Solinis. 2021. "Factors Influencing Care Pathways for Breast and Prostate Cancer in a Hospital Setting." International Journal of Environmental Research and Public Health 18, no. 15: 7913.
The use of artificial intelligence in health care has grown quickly. In this sense, we present our work related to the application of Natural Language Processing techniques, as a tool to analyze the sentiment perception of users who answered two questions from the CSQ-8 questionnaires with raw Spanish free-text. Their responses are related to mindfulness, which is a novel technique used to control stress and anxiety caused by different factors in daily life. As such, we proposed an online course where this method was applied in order to improve the quality of life of health care professionals in COVID 19 pandemic times. We also carried out an evaluation of the satisfaction level of the participants involved, with a view to establishing strategies to improve future experiences. To automatically perform this task, we used Natural Language Processing (NLP) models such as swivel embedding, neural networks, and transfer learning, so as to classify the inputs into the following three categories: negative, neutral, and positive. Due to the limited amount of data available—86 registers for the first and 68 for the second—transfer learning techniques were required. The length of the text had no limit from the user’s standpoint, and our approach attained a maximum accuracy of 93.02% and 90.53%, respectively, based on ground truth labeled by three experts. Finally, we proposed a complementary analysis, using computer graphic text representation based on word frequency, to help researchers identify relevant information about the opinions with an objective approach to sentiment. The main conclusion drawn from this work is that the application of NLP techniques in small amounts of data using transfer learning is able to obtain enough accuracy in sentiment analysis and text classification stages.
Mario Acosta; Gema Castillo-Sánchez; Begonya Garcia-Zapirain; Isabel De La Torre Díez; Manuel Franco-Martín. Sentiment Analysis Techniques Applied to Raw-Text Data from a Csq-8 Questionnaire about Mindfulness in Times of COVID-19 to Improve Strategy Generation. International Journal of Environmental Research and Public Health 2021, 18, 6408 .
AMA StyleMario Acosta, Gema Castillo-Sánchez, Begonya Garcia-Zapirain, Isabel De La Torre Díez, Manuel Franco-Martín. Sentiment Analysis Techniques Applied to Raw-Text Data from a Csq-8 Questionnaire about Mindfulness in Times of COVID-19 to Improve Strategy Generation. International Journal of Environmental Research and Public Health. 2021; 18 (12):6408.
Chicago/Turabian StyleMario Acosta; Gema Castillo-Sánchez; Begonya Garcia-Zapirain; Isabel De La Torre Díez; Manuel Franco-Martín. 2021. "Sentiment Analysis Techniques Applied to Raw-Text Data from a Csq-8 Questionnaire about Mindfulness in Times of COVID-19 to Improve Strategy Generation." International Journal of Environmental Research and Public Health 18, no. 12: 6408.
Colorectal cancer is one of the main causes of cancer incident cases and cancer deaths worldwide. Undetected colon polyps, be them benign or malignant, lead to late diagnosis of colorectal cancer. Computer aided devices have helped to decrease the polyp miss rate. The application of deep learning algorithms and techniques has escalated during this last decade. Many scientific studies are published to detect, localize, and classify colon polyps. We present here a brief review of the latest published studies. We compare the accuracy of these studies with our results obtained from training and testing three independent datasets using a convolutional neural network and autoencoder model. A train, validate and test split was performed for each dataset, 75%, 15%, and 15%, respectively. An accuracy of 0.937 was achieved for CVC-ColonDB, 0.951 for CVC-ClinicDB, and 0.967 for ETIS-LaribPolypDB. Our results suggest slight improvements compared to the algorithms used to date.
Ornela Bardhi; Daniel Sierra-Sosa; Begonya Garcia-Zapirain; Luis Bujanda. Deep Learning Models for Colorectal Polyps. Information 2021, 12, 245 .
AMA StyleOrnela Bardhi, Daniel Sierra-Sosa, Begonya Garcia-Zapirain, Luis Bujanda. Deep Learning Models for Colorectal Polyps. Information. 2021; 12 (6):245.
Chicago/Turabian StyleOrnela Bardhi; Daniel Sierra-Sosa; Begonya Garcia-Zapirain; Luis Bujanda. 2021. "Deep Learning Models for Colorectal Polyps." Information 12, no. 6: 245.
The aim of this study was to build a tool to analyze, using artificial intelligence, the sentiment perception of users who answered two questions from the CSQ – 8 questionnaires with raw Spanish free-text. Their responses are related to mindfulness, which is a novel technique used to control stress and anxiety caused by different factors in daily life. As such, we proposed an online course where this method was applied in order to improve the quality of life of health care professionals in COVID 19 pandemic times. We also carried out an evaluation of the satis-faction level of the participants involved, with a view to establishing strategies to improve fu-ture experiences. To automatically perform this task, we used Natural Language Processing (NLP) models such as swivel embedding, neural networks and transfer learning, so as to classify the inputs into the following 3 categories: negative, neutral and positive. Due to the lim-ited amount of data available - 86 registers for the first and 68 for the second - transfer learning techniques were required. The length of the text had no limit from the user’s standpoint, and our approach attained a maximum accuracy of 93.02 % and 90.53 % respectively based on ground truth labeled by 3 experts. Finally, we proposed a complementary analysis, using com-puter graphic text representation based on word frequency, to help researchers identify relevant information about the opinions with an objective approach to sentiment. The main conclusion drawn from this work is that the application of NLP techniques in small amounts of data using transfer learning is able to obtain enough accuracy in sentiment analysis and text classification stages
Mario Jojoa; Gema Castillo-Sánchez; Begonya Garcia-Zapirain; Isabel De La Torre Diez; Manuel Franco-Martín. Sentiment Analysis Techniques Applied to Raw-Text Data from a CSQ-8 Questionnaire About Mindfulness in Times of Covid-19 to Improve Strategy Generation. 2021, 1 .
AMA StyleMario Jojoa, Gema Castillo-Sánchez, Begonya Garcia-Zapirain, Isabel De La Torre Diez, Manuel Franco-Martín. Sentiment Analysis Techniques Applied to Raw-Text Data from a CSQ-8 Questionnaire About Mindfulness in Times of Covid-19 to Improve Strategy Generation. . 2021; ():1.
Chicago/Turabian StyleMario Jojoa; Gema Castillo-Sánchez; Begonya Garcia-Zapirain; Isabel De La Torre Diez; Manuel Franco-Martín. 2021. "Sentiment Analysis Techniques Applied to Raw-Text Data from a CSQ-8 Questionnaire About Mindfulness in Times of Covid-19 to Improve Strategy Generation." , no. : 1.
(1) Background: The COVID-19 pandemic has created a great impact on mental health in society. Considering the little attention paid by scientific studies to either students or university staff during lockdown, the current study has two aims: (a) to analyze the evolution of mental health and (b) to identify predictors of educational/professional experience and online learning/teaching experience. (2) Methods: 1084 university students and 554 staff in total from four different countries (Spain, Colombia, Chile and Nicaragua) participated in the study, affiliated with nine different universities, four of them Spanish and one of which was online. We used an online survey known as LockedDown, which consists of 82 items, analyzed with classical multiple regression analyses and machine learning techniques. (3) Results: Stress level and feelings of anxiety and depression of students and staff either increased or remained over the weeks. A better online learning experience for university students was associated with the age, perception of the experience as beneficial and support of the university. (4) Conclusions: The study has shown evidence of the emotional impact and quality of life for both students and staff. For students, the evolution of feelings of anxiety and depression, as well as the support offered by the university affected the educational experience and online learning. For staff who experienced a positive professional experience, with access to services and products, the quality-of-life levels were maintained.
Mario Jojoa; Esther Lazaro; Begonya Garcia-Zapirain; Marino Gonzalez; Elena Urizar. The Impact of COVID 19 on University Staff and Students from Iberoamerica: Online Learning and Teaching Experience. International Journal of Environmental Research and Public Health 2021, 18, 5820 .
AMA StyleMario Jojoa, Esther Lazaro, Begonya Garcia-Zapirain, Marino Gonzalez, Elena Urizar. The Impact of COVID 19 on University Staff and Students from Iberoamerica: Online Learning and Teaching Experience. International Journal of Environmental Research and Public Health. 2021; 18 (11):5820.
Chicago/Turabian StyleMario Jojoa; Esther Lazaro; Begonya Garcia-Zapirain; Marino Gonzalez; Elena Urizar. 2021. "The Impact of COVID 19 on University Staff and Students from Iberoamerica: Online Learning and Teaching Experience." International Journal of Environmental Research and Public Health 18, no. 11: 5820.
Physicians depend on their insight and experience and on a fundamentally indicative or symptomatic approach to decide on the possible ailment of a patient. However, numerous phases of problem identification and longer strategies can prompt a longer time for consulting and can subsequently cause other patients that require attention to wait for longer. This can bring about pressure and tension concerning those patients. In this study, we focus on developing a decision-support system for diagnosing the symptoms as a result of hearing loss. The model is implemented by utilizing machine learning techniques. The Frequent Pattern Growth (FP-Growth) algorithm is used as a feature transformation method and the multivariate Bernoulli naïve Bayes classification model as the classifier. To find the correlation that exists between the hearing thresholds and symptoms of hearing loss, the FP-Growth and association rule algorithms were first used to experiment with small sample and large sample datasets. The result of these two experiments showed the existence of this relationship, and that the performance of the hybrid of the FP-Growth and naïve Bayes algorithms in identifying hearing-loss symptoms was found to be efficient, with a very small error rate. The average accuracy rate and average error rate for the multivariate Bernoulli model with FP-Growth feature transformation, using five training sets, are 98.25% and 1.73%, respectively.
Mohd Abd Ghani; Nasir Noma; Mazin Mohammed; Karrar Abdulkareem; Begonya Garcia-Zapirain; Mashael Maashi; Salama Mostafa. Innovative Artificial Intelligence Approach for Hearing-Loss Symptoms Identification Model Using Machine Learning Techniques. Sustainability 2021, 13, 5406 .
AMA StyleMohd Abd Ghani, Nasir Noma, Mazin Mohammed, Karrar Abdulkareem, Begonya Garcia-Zapirain, Mashael Maashi, Salama Mostafa. Innovative Artificial Intelligence Approach for Hearing-Loss Symptoms Identification Model Using Machine Learning Techniques. Sustainability. 2021; 13 (10):5406.
Chicago/Turabian StyleMohd Abd Ghani; Nasir Noma; Mazin Mohammed; Karrar Abdulkareem; Begonya Garcia-Zapirain; Mashael Maashi; Salama Mostafa. 2021. "Innovative Artificial Intelligence Approach for Hearing-Loss Symptoms Identification Model Using Machine Learning Techniques." Sustainability 13, no. 10: 5406.
Opinion Mining (OM) is a field of Natural Language Processing (NLP) that aims to capture human sentiment in the given text. With the ever-spreading of online purchasing websites, micro-blogging sites, and social media platforms, OM in online social media platforms has picked the interest of thousands of scientific researchers. Because the reviews, tweets and blogs acquired from these social media networks, act as a significant source for enhancing the decision making process. The obtained textual data (reviews, tweets, or blogs) are classified into three different class labels which are negative, neutral and positive for analyzing and extracting relevant information from the given dataset. In this contribution, we introduce an innovative MapReduce improved weighted ID3 decision tree classification approach for OM, which consists mainly of three aspects: Firstly We have used several feature extractors to efficiently detect and capture the relevant data from the given tweets, including N-grams or character-level, Bag-Of-Words, word embedding (GloVe, Word2Vec), FastText, and TF-IDF. Secondly, we have applied a multiple feature selector to reduce the high feature’s dimensionality, including Chi-square, Gain Ratio, Information Gain, and Gini Index. Finally, we have employed the obtained features to carry out the classification task using an improved ID3 decision tree classifier, which aims to calculate the weighted information gain instead of information gain used in traditional ID3. In other words, to measure the weighted information gain for the current conditioned feature, we follow two steps: First, we compute the weighted correlation function of the current conditioned feature. Second, we multiply the obtained weighted correlation function by the information gain of this current conditioned feature. This work is implemented in a distributed environment using the Hadoop framework, with its programming framework MapReduce and its distributed file system HDFS. Its primary goal is to enhance the performance of a well-known ID3 classifier in terms of accuracy, execution time, and ability to handle the massive datasets. We have carried out several experiences that aims to assess the effectiveness of our suggested classifier compared to some other contributions chosen from the literature. The experimental results demonstrated that our ID3 classifier works better on COVID-19_Sentiments dataset than other classifiers in terms of Recall (85.72 %), specificity (86.51 %), error rate (11.18 %), false-positive rate (13.49 %), execution time (15.95s), kappa statistic (87.69 %), F1-score (85.54 %), classification rate (88.82 %), false-negative rate (14.28 %), precision rate (86.67 %), convergence (it convergent towards the iteration 90), stability (it is more stable with mean deviation standard equal to 0.12 %), and complexity (it requires much lower time and space computational complexity).
Fatima Es-Sabery; Khadija Es-Sabery; Junaid Qadir; Beatriz Sainz-De-Abajo; Abdellatif Hair; Begona Garcia-Zapirain; Isabel De La Torre-Diez. A MapReduce Opinion Mining for COVID-19-Related Tweets Classification Using Enhanced ID3 Decision Tree Classifier. IEEE Access 2021, 9, 1 -1.
AMA StyleFatima Es-Sabery, Khadija Es-Sabery, Junaid Qadir, Beatriz Sainz-De-Abajo, Abdellatif Hair, Begona Garcia-Zapirain, Isabel De La Torre-Diez. A MapReduce Opinion Mining for COVID-19-Related Tweets Classification Using Enhanced ID3 Decision Tree Classifier. IEEE Access. 2021; 9 ():1-1.
Chicago/Turabian StyleFatima Es-Sabery; Khadija Es-Sabery; Junaid Qadir; Beatriz Sainz-De-Abajo; Abdellatif Hair; Begona Garcia-Zapirain; Isabel De La Torre-Diez. 2021. "A MapReduce Opinion Mining for COVID-19-Related Tweets Classification Using Enhanced ID3 Decision Tree Classifier." IEEE Access 9, no. : 1-1.
This article proposes an example of a multiplatform interactive serious game, which is an additional tool and assistant used in the rehabilitation of patients with musculoskeletal system problems. In medicine, any actions and procedures aimed at helping the rehabilitation of patients should entail the most comfortable, but at the same time, effective approach. Regardless of how these actions are orientated, whether for rehabilitation following surgery, fractures, any problems with the musculoskeletal system, or just support for the elderly, rehabilitation methods undoubtedly have good goals, although often the process itself can cause all kinds of discomfort and aversion among patients. This paper presents an interactive platform which enables a slightly different approach to be applied in terms of routine rehabilitation activities and this will help make the process more exciting. The main feature of the system is that it works in several ways: for normal everyday use at home, or for more in-depth observation of various biological parameters, such as heart rate, temperature, and so on. The basic component of the system is the real-time tracking system of the body position, which constitutes both a way to control the game (controller) and a means to analyze the player’s activity. As for the closer control of rehabilitation, the platform also provides the opportunity for medical personnel to monitor the player in real time, with all the data obtained from the game being used for subsequent analysis and comparison. Following several laboratory tests and feedback analysis, the progress indicators are quite encouraging in terms of greater patient interest in this kind of interaction, and effectiveness of the developed platform is also on average about 30–50% compared to conventional exercises, which makes it more attractive in terms of patient support.
Serhii Shapoval; Begoña García Zapirain; Amaia Mendez Zorrilla; Iranzu Mugueta-Aguinaga. Biofeedback Applied to Interactive Serious Games to Monitor Frailty in an Elderly Population. Applied Sciences 2021, 11, 3502 .
AMA StyleSerhii Shapoval, Begoña García Zapirain, Amaia Mendez Zorrilla, Iranzu Mugueta-Aguinaga. Biofeedback Applied to Interactive Serious Games to Monitor Frailty in an Elderly Population. Applied Sciences. 2021; 11 (8):3502.
Chicago/Turabian StyleSerhii Shapoval; Begoña García Zapirain; Amaia Mendez Zorrilla; Iranzu Mugueta-Aguinaga. 2021. "Biofeedback Applied to Interactive Serious Games to Monitor Frailty in an Elderly Population." Applied Sciences 11, no. 8: 3502.
This research focuses on the development of a system for measuring finger joint angles based on camera image and is intended for work within the field of medicine to track the movement and limits of hand mobility in multiple sclerosis. Measuring changes in hand mobility allows the progress of the disease and its treatment process to be monitored. A static RGB camera without depth vision was used in the system developed, with the system receiving only the image from the camera and no other input data. The research focuses on the analysis of each image in the video stream independently of other images from that stream, and 12 measured hand parameters were chosen as follows: 3 joint angles for the index finger, 3 joint angles for the middle finger, 3 joint angles for the ring finger, and 3 joint angles for the pinky finger. Convolutional neural networks were used to analyze the information received from the camera, and the research considers neural networks based on different architectures and their combinations as follows: VGG16, MobileNet, MobileNetV2, InceptionV3, DenseNet, ResNet, and convolutional pose machine. The final neural network used for image analysis was a modernized neural network based on MobileNetV2, which obtained the best mean absolute error value of 4.757 degrees. Additionally, the mean square error was 67.279 and the root mean square error was 8.202 degrees. This neural network analyzed a single image from the camera without using other sensors. For its part, the input image had a resolution of 512 by 512 pixels, and was processed by the neural network in 7–15 ms by GPU Nvidia 2080ti. The resulting neural network developed can measure finger joint angle values for a hand with non-standard parameters and positions.
Dmitry Viatkin; Begonya Garcia-Zapirain; Amaia Méndez Zorrilla. Deep Learning Techniques Applied to Predict and Measure Finger Movement in Patients with Multiple Sclerosis. Applied Sciences 2021, 11, 3137 .
AMA StyleDmitry Viatkin, Begonya Garcia-Zapirain, Amaia Méndez Zorrilla. Deep Learning Techniques Applied to Predict and Measure Finger Movement in Patients with Multiple Sclerosis. Applied Sciences. 2021; 11 (7):3137.
Chicago/Turabian StyleDmitry Viatkin; Begonya Garcia-Zapirain; Amaia Méndez Zorrilla. 2021. "Deep Learning Techniques Applied to Predict and Measure Finger Movement in Patients with Multiple Sclerosis." Applied Sciences 11, no. 7: 3137.
COVID-19 had led to severe clinical manifestations. In the current scenario, 98 794 942 people are infected, and it has responsible for 2 124 193 deaths around the world as reported by World Health Organization on 25 January 2021. Telemedicine has become a critical technology for providing medical care to patients by trying to reduce transmission of the virus among patients, families, and doctors. The economic consequences of coronavirus have affected the entire world and disrupted daily life in many countries. The development of telemedicine applications and eHealth services can significantly help to manage pandemic worldwide better. Consequently, the main objective of this paper is to present a systematic review of the implementation of telemedicine and e-health systems in the combat to COVID-19. The main contribution is to present a comprehensive description of the state of the art considering the domain areas, organizations, funding agencies, researcher units and authors involved. The results show that the United States and China have the most significant number of studies representing 42.11% and 31.58%, respectively. Furthermore, 35 different research units and 9 funding agencies are involved in the application of telemedicine systems to combat COVID-19.
Susel Góngora Alonso; Goncalo Marques; Isidro Barrachina; Begonya Garcia-Zapirain; Jon Arambarri; Javier Cabo Salvador; Isabel De La Torre Díez. Telemedicine and e-Health research solutions in literature for combatting COVID-19: a systematic review. Health and Technology 2021, 11, 257 -266.
AMA StyleSusel Góngora Alonso, Goncalo Marques, Isidro Barrachina, Begonya Garcia-Zapirain, Jon Arambarri, Javier Cabo Salvador, Isabel De La Torre Díez. Telemedicine and e-Health research solutions in literature for combatting COVID-19: a systematic review. Health and Technology. 2021; 11 (2):257-266.
Chicago/Turabian StyleSusel Góngora Alonso; Goncalo Marques; Isidro Barrachina; Begonya Garcia-Zapirain; Jon Arambarri; Javier Cabo Salvador; Isabel De La Torre Díez. 2021. "Telemedicine and e-Health research solutions in literature for combatting COVID-19: a systematic review." Health and Technology 11, no. 2: 257-266.
At present, with the growing number of Web 2.0 platforms such as Instagram, Facebook, and Twitter, users honestly communicate their opinions and ideas about events, services, and products. Owing to this rise in the number of social platforms and their extensive use by people, enormous amounts of data are produced hourly. However, sentiment analysis or opinion mining is considered as a useful tool that aims to extract the emotion and attitude from the user-posted data on social media platforms by using different computational methods to linguistic terms and various Natural Language Processing (NLP). Therefore, enhancing text sentiment classification accuracy has become feasible, and an interesting research area for many community researchers. In this study, a new Fuzzy Deep Learning Classifier (FDLC) is suggested for improving the performance of data-sentiment classification. Our proposed FDLC integrates Convolutional Neural Network (CNN) to build an effective automatic process for extracting the features from collected unstructured data and Feedforward Neural Network (FFNN) to compute both positive and negative sentimental scores. Then, we used the Mamdani Fuzzy System (MFS) as a fuzzy classifier to classify the outcomes of the two used deep (CNN+FFNN) learning models in three classes, which are: Neutral, Negative, and Positive. Also, to prevent the long execution time taking by our hybrid proposed FDLC, we have implemented our proposal under the Hadoop cluster. An experimental comparative study between our FDLC and some other suggestions from the literature is performed to demonstrate our offered classifier’s effectiveness. The empirical result proved that our FDLC performs better than other classifiers in terms of true positive rate, true negative rate, false positive rate, false negative rate, error rate, precision, classification rate, kappa statistic, F1-score and time consumption, complexity, convergence, and stability.
Fatima Es-Sabery; Abdellatif Hair; Junaid Qadir; Beatriz Sainz-De-Abajo; Begona Garcia-Zapirain; Isabel De La Torre-Díez. Sentence-Level Classification Using Parallel Fuzzy Deep Learning Classifier. IEEE Access 2021, 9, 17943 -17985.
AMA StyleFatima Es-Sabery, Abdellatif Hair, Junaid Qadir, Beatriz Sainz-De-Abajo, Begona Garcia-Zapirain, Isabel De La Torre-Díez. Sentence-Level Classification Using Parallel Fuzzy Deep Learning Classifier. IEEE Access. 2021; 9 ():17943-17985.
Chicago/Turabian StyleFatima Es-Sabery; Abdellatif Hair; Junaid Qadir; Beatriz Sainz-De-Abajo; Begona Garcia-Zapirain; Isabel De La Torre-Díez. 2021. "Sentence-Level Classification Using Parallel Fuzzy Deep Learning Classifier." IEEE Access 9, no. : 17943-17985.
The most commonly injured ligament in the human body is an anterior cruciate ligament (ACL). ACL injury is standard among the football, basketball and soccer players. The study aims to detect anterior cruciate ligament injury in an early stage via efficient and thorough automatic magnetic resonance imaging without involving radiologists, through a deep learning method. The proposed approach in this paper used a customized 14 layers ResNet-14 architecture of convolutional neural network (CNN) with six different directions by using class balancing and data augmentation. The performance was evaluated using accuracy, sensitivity, specificity, precision and F1 score of our customized ResNet-14 deep learning architecture with hybrid class balancing and real-time data augmentation after 5-fold cross-validation, with results of 0.920%, 0.916%, 0.946%, 0.916% and 0.923%, respectively. For our proposed ResNet-14 CNN the average area under curves (AUCs) for healthy tear, partial tear and fully ruptured tear had results of 0.980%, 0.970%, and 0.999%, respectively. The proposing diagnostic results indicated that our model could be used to detect automatically and evaluate ACL injuries in athletes using the proposed deep-learning approach.
Mazhar Javed Awan; Mohd Mohd Rahim; Naomie Salim; Mazin Mohammed; Begonya Garcia-Zapirain; Karrar Abdulkareem. Efficient Detection of Knee Anterior Cruciate Ligament from Magnetic Resonance Imaging Using Deep Learning Approach. Diagnostics 2021, 11, 105 .
AMA StyleMazhar Javed Awan, Mohd Mohd Rahim, Naomie Salim, Mazin Mohammed, Begonya Garcia-Zapirain, Karrar Abdulkareem. Efficient Detection of Knee Anterior Cruciate Ligament from Magnetic Resonance Imaging Using Deep Learning Approach. Diagnostics. 2021; 11 (1):105.
Chicago/Turabian StyleMazhar Javed Awan; Mohd Mohd Rahim; Naomie Salim; Mazin Mohammed; Begonya Garcia-Zapirain; Karrar Abdulkareem. 2021. "Efficient Detection of Knee Anterior Cruciate Ligament from Magnetic Resonance Imaging Using Deep Learning Approach." Diagnostics 11, no. 1: 105.
Background Melanoma has become more widespread over the past 30 years and early detection is a major factor in reducing mortality rates associated with this type of skin cancer. Therefore, having access to an automatic, reliable system that is able to detect the presence of melanoma via a dermatoscopic image of lesions and/or skin pigmentation can be a very useful tool in the area of medical diagnosis. Methods Among state-of-the-art methods used for automated or computer assisted medical diagnosis, attention should be drawn to Deep Learning based on Convolutional Neural Networks, wherewith segmentation, classification and detection systems for several diseases have been implemented. The method proposed in this paper involves an initial stage that automatically crops the region of interest within a dermatoscopic image using the Mask and Region-based Convolutional Neural Network technique, and a second stage based on a ResNet152 structure, which classifies lesions as either “benign” or “malignant”. Results Training, validation and testing of the proposed model was carried out using the database associated to the challenge set out at the 2017 International Symposium on Biomedical Imaging. On the test data set, the proposed model achieves an increase in accuracy and balanced accuracy of 3.66% and 9.96%, respectively, with respect to the best accuracy and the best sensitivity/specificity ratio reported to date for melanoma detection in this challenge. Additionally, unlike previous models, the specificity and sensitivity achieve a high score (greater than 0.8) simultaneously, which indicates that the model is good for accurate discrimination between benign and malignant lesion, not biased towards any of those classes. Conclusions The results achieved with the proposed model suggest a significant improvement over the results obtained in the state of the art as far as performance of skin lesion classifiers (malignant/benign) is concerned.
Mario Fernando Jojoa Acosta; Liesle Yail Caballero Tovar; Maria Begonya Garcia-Zapirain; Winston Spencer Percybrooks. Melanoma diagnosis using deep learning techniques on dermatoscopic images. BMC Medical Imaging 2021, 21, 1 -11.
AMA StyleMario Fernando Jojoa Acosta, Liesle Yail Caballero Tovar, Maria Begonya Garcia-Zapirain, Winston Spencer Percybrooks. Melanoma diagnosis using deep learning techniques on dermatoscopic images. BMC Medical Imaging. 2021; 21 (1):1-11.
Chicago/Turabian StyleMario Fernando Jojoa Acosta; Liesle Yail Caballero Tovar; Maria Begonya Garcia-Zapirain; Winston Spencer Percybrooks. 2021. "Melanoma diagnosis using deep learning techniques on dermatoscopic images." BMC Medical Imaging 21, no. 1: 1-11.
Background Mobile health apps are used to improve the quality of health care. These apps are changing the current scenario in health care, and their numbers are increasing. Objective We wanted to perform an analysis of the current status of mobile health technologies and apps for medical emergencies. We aimed to synthesize the existing body of knowledge to provide relevant insights for this topic. Moreover, we wanted to identify common threads and gaps to support new challenging, interesting, and relevant research directions. Methods We reviewed the main relevant papers and apps available in the literature. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology was used in this review. The search criteria were adopted using systematic methods to select papers and apps. On one hand, a bibliographic review was carried out in different search databases to collect papers related to each application in the health emergency field using defined criteria. On the other hand, a review of mobile apps in two virtual storage platforms (Google Play Store and Apple App Store) was carried out. The Google Play Store and Apple App Store are related to the Android and iOS operating systems, respectively. Results In the literature review, 28 papers in the field of medical emergency were included. These studies were collected and selected according to established criteria. Moreover, we proposed a taxonomy using six groups of applications. In total, 324 mobile apps were found, with 192 identified in the Google Play Store and 132 identified in the Apple App Store. Conclusions We found that all apps in the Google Play Store were free, and 73 apps in the Apple App Store were paid, with the price ranging from US $0.89 to US $5.99. Moreover, 39% (11/28) of the included studies were related to warning systems for emergency services and 21% (6/28) were associated with disaster management apps.
Alejandro Plaza Roncero; Gonçalo Marques; Beatriz Sainz-De-Abajo; Francisco Martín-Rodríguez; Carlos Del Pozo Vegas; Begonya Garcia-Zapirain; Isabel de la Torre-Díez. Mobile Health Apps for Medical Emergencies: Systematic Review. JMIR mHealth and uHealth 2020, 8, e18513 .
AMA StyleAlejandro Plaza Roncero, Gonçalo Marques, Beatriz Sainz-De-Abajo, Francisco Martín-Rodríguez, Carlos Del Pozo Vegas, Begonya Garcia-Zapirain, Isabel de la Torre-Díez. Mobile Health Apps for Medical Emergencies: Systematic Review. JMIR mHealth and uHealth. 2020; 8 (12):e18513.
Chicago/Turabian StyleAlejandro Plaza Roncero; Gonçalo Marques; Beatriz Sainz-De-Abajo; Francisco Martín-Rodríguez; Carlos Del Pozo Vegas; Begonya Garcia-Zapirain; Isabel de la Torre-Díez. 2020. "Mobile Health Apps for Medical Emergencies: Systematic Review." JMIR mHealth and uHealth 8, no. 12: e18513.
Background: HealthyAIR is a tool that detects pollution risk in real life. The target population is people with cardiorespiratory complications who are especially susceptible to the current COVID-19. The goal is to empower people by controlling air pollution everywhere to minimize the risk of having a seizure. Methods: We measured the social impact of the HealthyAIR tool using a Likert scale survey with two groups: professionals (engineers/healthcare) and end-users. We assessed the data in accordance with the indicators for social impact defined for the Key Impact Pathways introduced by the European Commission for Horizon Europe, and the criteria of the Social Impact Open Repository (SIOR). Results: Professionals highlight the fact that they “totally agree” (33.33%) and “agree” (26.67%) that HealthyAIR could help authorities improve their health prevention policies and programs. Users considered the tool to be “very useful” (38.46%) and “quite useful” (42.31%), which denotes its necessity. Conclusions: professionals and end users see HealthyAIR as a great preventative tool, with the former seeing it as a way to avoid seizures in their patients who, in this COVID-19 era, are particularly sensitive to any cardiorespiratory health problem. However, users suggest improving the user’s manual to make it more understandable.
Antonia Moreno Cano; Rafael Romón Sagredo; Rocío García-Carrión; Begonya Garcia-Zapirain. Social Impact Assessment of HealthyAIR Tool for Real-Time Detection of Pollution Risk. Sustainability 2020, 12, 9856 .
AMA StyleAntonia Moreno Cano, Rafael Romón Sagredo, Rocío García-Carrión, Begonya Garcia-Zapirain. Social Impact Assessment of HealthyAIR Tool for Real-Time Detection of Pollution Risk. Sustainability. 2020; 12 (23):9856.
Chicago/Turabian StyleAntonia Moreno Cano; Rafael Romón Sagredo; Rocío García-Carrión; Begonya Garcia-Zapirain. 2020. "Social Impact Assessment of HealthyAIR Tool for Real-Time Detection of Pollution Risk." Sustainability 12, no. 23: 9856.
The outbreaks of Coronavirus (COVID-19) epidemic have increased the pressure on healthcare and medical systems worldwide. The timely diagnosis of infected patients is a critical step to limit the spread of the COVID-19 epidemic. The chest radiography imaging has shown to be an effective screening technique in diagnosing the COVID-19 epidemic. To reduce the pressure on radiologists and control of the epidemic, fast and accurate a hybrid deep learning framework for diagnosing COVID-19 virus in chest X-ray images is developed and termed as the COVID-CheXNet system. First, the contrast of the X-ray image was enhanced and the noise level was reduced using the contrast-limited adaptive histogram equalization and Butterworth bandpass filter, respectively. This was followed by fusing the results obtained from two different pre-trained deep learning models based on the incorporation of a ResNet34 and high-resolution network model trained using a large-scale dataset. Herein, the parallel architecture was considered, which provides radiologists with a high degree of confidence to discriminate between the healthy and COVID-19 infected people. The proposed COVID-CheXNet system has managed to correctly and accurately diagnose the COVID-19 patients with a detection accuracy rate of 99.99%, sensitivity of 99.98%, specificity of 100%, precision of 100%, F1-score of 99.99%, MSE of 0.011%, and RMSE of 0.012% using the weighted sum rule at the score-level. The efficiency and usefulness of the proposed COVID-CheXNet system are established along with the possibility of using it in real clinical centers for fast diagnosis and treatment supplement, with less than 2 s per image to get the prediction result.
Alaa S. Al-Waisy; Shumoos Al-Fahdawi; Mazin Abed Mohammed; Karrar Hameed Abdulkareem; Salama A. Mostafa; Mashael S. Maashi; Muhammad Arif; Begonya Garcia-Zapirain. COVID-CheXNet: hybrid deep learning framework for identifying COVID-19 virus in chest X-rays images. Soft Computing 2020, 1 -16.
AMA StyleAlaa S. Al-Waisy, Shumoos Al-Fahdawi, Mazin Abed Mohammed, Karrar Hameed Abdulkareem, Salama A. Mostafa, Mashael S. Maashi, Muhammad Arif, Begonya Garcia-Zapirain. COVID-CheXNet: hybrid deep learning framework for identifying COVID-19 virus in chest X-rays images. Soft Computing. 2020; ():1-16.
Chicago/Turabian StyleAlaa S. Al-Waisy; Shumoos Al-Fahdawi; Mazin Abed Mohammed; Karrar Hameed Abdulkareem; Salama A. Mostafa; Mashael S. Maashi; Muhammad Arif; Begonya Garcia-Zapirain. 2020. "COVID-CheXNet: hybrid deep learning framework for identifying COVID-19 virus in chest X-rays images." Soft Computing , no. : 1-16.
The realm of cloud computing has revolutionized access to cloud resources and their utilization and applications over the Internet. However, deploying cloud computing for delay critical applications and reducing the delay in access to the resources are challenging. The Mobile Edge Computing (MEC) paradigm is one of the effective solutions, which brings the cloud computing services to the proximity of the edge network and leverages the available resources. This paper presents a survey of the latest and state-of-the-art algorithms, techniques, and concepts of MEC. The proposed work is unique in that the most novel algorithms are considered, which are not considered by the existing surveys. Moreover, the chosen novel literature of the existing researchers is classified in terms of performance metrics by describing the realms of promising performance and the regions where the margin of improvement exists for future investigation for the future researchers. This also eases the choice of a particular algorithm for a particular application. As compared to the existing surveys, the bibliometric overview is provided, which is further helpful for the researchers, engineers, and scientists for a thorough insight, application selection, and future consideration for improvement. In addition, applications related to the MEC platform are presented. Open research challenges, future directions, and lessons learned in area of the MEC are provided for further future investigation.
Junaid Qadir; Beatriz Sainz-De-Abajo; Anwar Khan; Begona Garcia-Zapirain; Isabel De La Torre-Diez; Hasan Mahmood. Towards Mobile Edge Computing: Taxonomy, Challenges, Applications and Future Realms. IEEE Access 2020, 8, 189129 -189162.
AMA StyleJunaid Qadir, Beatriz Sainz-De-Abajo, Anwar Khan, Begona Garcia-Zapirain, Isabel De La Torre-Diez, Hasan Mahmood. Towards Mobile Edge Computing: Taxonomy, Challenges, Applications and Future Realms. IEEE Access. 2020; 8 (99):189129-189162.
Chicago/Turabian StyleJunaid Qadir; Beatriz Sainz-De-Abajo; Anwar Khan; Begona Garcia-Zapirain; Isabel De La Torre-Diez; Hasan Mahmood. 2020. "Towards Mobile Edge Computing: Taxonomy, Challenges, Applications and Future Realms." IEEE Access 8, no. 99: 189129-189162.
This paper presents a Multilayer Perceptron and Support Vector Machine algorithms approach to predict the number of COVID19 infections in different countries of America. It intends to serve as a tool for decision-making and tackling the pandemic that the world is currently facing. The models were trained and tested using open data from the European Union repository where a time series of confirmed contagious cases was modeled until May 25, 2020. The hyperparameters as number of neurons per layer were set up using a tabu list algorithm. The countries selected to carry out the study were Brazil, Chile, Colombia, Mexico, Peru and the United States. The metrics used are Pearson's correlation coefficient (CP), Mean Absolute Error (MAE), and Mean Percentage Error (MPE). For the testing stage we obtained the following results: Brazil, CP=0.65, MAE=2508 and MPE=17%; Chile, CP=0.64, MAE=504, MPE=16%; Colombia, CP=0.83, MAE=76, MPE=9%; Mexico, CP=0.77, MAE=231, MPE=9%; Peru, CP=0.76, MAE=686, MPE=18% and the United States of America, CP=0.93, MAE=799, MPE=4%. This resulted in powerful machine learning tools although it is necessary to use specific algorithms depending on the data and the stage of the country’s pandemic.
Mario Jojoa; Begoña Garcia-Zapirain. Forecasting COVID 19 Confirmed Cases Using Machine Learning: the Case of America. 2020, 1 .
AMA StyleMario Jojoa, Begoña Garcia-Zapirain. Forecasting COVID 19 Confirmed Cases Using Machine Learning: the Case of America. . 2020; ():1.
Chicago/Turabian StyleMario Jojoa; Begoña Garcia-Zapirain. 2020. "Forecasting COVID 19 Confirmed Cases Using Machine Learning: the Case of America." , no. : 1.
The discrimination of non-focal class (NFC) and focal class (FC), is vital in localizing the epileptogenic zone (EZ) during neurosurgery. In the conventional diagnosis method, the neurologist has to visually examine the long hour electroencephalogram (EEG) signals, which consumes time and is prone to error. Hence, in this present work, automated diagnosis of FC EEG signals from NFC EEG signals is developed using the Fast Walsh–Hadamard Transform (FWHT) method, entropies, and artificial neural network (ANN). The FWHT analyzes the EEG signals in the frequency domain and decomposes it into the Hadamard coefficients. Five different nonlinear features, namely approximate entropy (ApEn), log-energy entropy (LogEn), fuzzy entropy (FuzzyEn), sample entropy (SampEn), and permutation entropy (PermEn) are extracted from the decomposed Hadamard coefficients. The extracted features detail the nonlinearity in the NFC and the FC EEG signals. The judicious entropy features are supplied to the ANN classifier, with a 10-fold cross-validation method to classify the NFC and FC classes. Two publicly available datasets such as the University of Bonn and Bern-Barcelona dataset are used to evaluate the proposed approach. A maximum sensitivity of 99.70%, the accuracy of 99.50%, and specificity of 99.30% with the 3750 pairs of NFC and FC signal are achieved using the Bern-Barcelona dataset, while the accuracy of 92.80%, the sensitivity of 91%, and specificity of 94.60% is achieved using University of Bonn dataset. Compared to the existing technique, the proposed approach attained a maximum classification performance in both the dataset.
Prasanna J.; M. S. P. Subathra; Mazin Abed Mohammed; Mashael S. Maashi; Begonya Garcia-Zapirain; N. J. Sairamya; S. Thomas George. Detection of Focal and Non-Focal Electroencephalogram Signals Using Fast Walsh-Hadamard Transform and Artificial Neural Network. Sensors 2020, 20, 4952 .
AMA StylePrasanna J., M. S. P. Subathra, Mazin Abed Mohammed, Mashael S. Maashi, Begonya Garcia-Zapirain, N. J. Sairamya, S. Thomas George. Detection of Focal and Non-Focal Electroencephalogram Signals Using Fast Walsh-Hadamard Transform and Artificial Neural Network. Sensors. 2020; 20 (17):4952.
Chicago/Turabian StylePrasanna J.; M. S. P. Subathra; Mazin Abed Mohammed; Mashael S. Maashi; Begonya Garcia-Zapirain; N. J. Sairamya; S. Thomas George. 2020. "Detection of Focal and Non-Focal Electroencephalogram Signals Using Fast Walsh-Hadamard Transform and Artificial Neural Network." Sensors 20, no. 17: 4952.
Breast cancer is one of the major public health issues and is considered a leading cause of cancer-related deaths among women worldwide. Its early diagnosis can effectively help in increasing the chances of survival rate. To this end, biopsy is usually followed as a gold standard approach in which tissues are collected for microscopic analysis. However, the histopathological analysis of breast cancer is non-trivial, labor-intensive, and may lead to a high degree of disagreement among pathologists. Therefore, an automatic diagnostic system could assist pathologists to improve the effectiveness of diagnostic processes. This paper presents an ensemble deep learning approach for the definite classification of non-carcinoma and carcinoma breast cancer histopathology images using our collected dataset. We trained four different models based on pre-trained VGG16 and VGG19 architectures. Initially, we followed 5-fold cross-validation operations on all the individual models, namely, fully-trained VGG16, fine-tuned VGG16, fully-trained VGG19, and fine-tuned VGG19 models. Then, we followed an ensemble strategy by taking the average of predicted probabilities and found that the ensemble of fine-tuned VGG16 and fine-tuned VGG19 performed competitive classification performance, especially on the carcinoma class. The ensemble of fine-tuned VGG16 and VGG19 models offered sensitivity of 97.73% for carcinoma class and overall accuracy of 95.29%. Also, it offered an F1 score of 95.29%. These experimental results demonstrated that our proposed deep learning approach is effective for the automatic classification of complex-natured histopathology images of breast cancer, more specifically for carcinoma images.
Zabit Hameed; Sofia Zahia; Begonya Garcia-Zapirain; José Javier Aguirre; Ana María Vanegas. Breast Cancer Histopathology Image Classification Using an Ensemble of Deep Learning Models. Sensors 2020, 20, 4373 .
AMA StyleZabit Hameed, Sofia Zahia, Begonya Garcia-Zapirain, José Javier Aguirre, Ana María Vanegas. Breast Cancer Histopathology Image Classification Using an Ensemble of Deep Learning Models. Sensors. 2020; 20 (16):4373.
Chicago/Turabian StyleZabit Hameed; Sofia Zahia; Begonya Garcia-Zapirain; José Javier Aguirre; Ana María Vanegas. 2020. "Breast Cancer Histopathology Image Classification Using an Ensemble of Deep Learning Models." Sensors 20, no. 16: 4373.