This study evaluated the maturity of Digital Transformation (DT) in a Higher Education Institution (HEI), analyzing how teachers, students, administrators and managers perceive various dimensions of DT, including Sociocultural, Teaching-Learning, Digital Governance, Research-Innovation, Academic Management, Administrative Management, Institutional Image-Marketing and University Extension. A quantitative approach was used, applying four structured surveys validated by experts to 338 participants from a HEI, which were processed by applying Spearman’s correlation analysis. It was found that digital maturity varies significantly according to the actor and the dimension considered. For teachers, Digital Governance shows strong links with University Extension and Research, suggesting that digitalization enhances both social projection and academic collaboration. Students perceive the importance of Institutional Image-Marketing in creating a positive presence of the institution. As for administrative staff, administrative management is strongly related to Institutional Image, reflecting the impact of internal efficiency on public perception. Managers, on the other hand, emphasize the role of University Extension in strengthening institutional prestige. The study concludes that DT in HEIs is multifactorial and recommends adopting customized digital maturity strategies that respond to the needs of each actor, thus facilitating an effective and sustainable digital transformation.

The timely diagnosis of brain tumors is currently a complicated task. The objective was to build an image classification model to detect the existence or not of brain tumors by adding a classification header to a ResNet-50 architecture. The CRISP-DM methodology was used for data mining. A dataset of 3847 brain MRI images was used, 2770 images for training, 500 for validation, and 577 for testing. The images were resized to a 256 × 256 scale and then a data generator is created that is responsible for dividing pixels by 255. The training was performed and then the evaluation process was carried out, obtaining an accuracy percentage of 92% and a precision of 94% in the evaluation process. It is concluded that the proposed CNN model composed of a head with a ResNet50 architecture and a seven-layer convolutional network achieves adequate accuracy, becoming an efficient and complementary proposal to other models developed in previous works.

In the current global context, there has been a significant increase in respiratory system diseases, particularly pneumonia. This disease has a higher incidence of mortality in children under five years old and adults over 60 years old because it leads to complications if not treated in time. This research leverages convolutional neural networks (CNNs) to classify images, specifically to detect the presence of pneumonia. The data processing methodology utilized in this study is CRISP-DM. The dataset consists of 5,856 images of anteroposterior chest X-rays downloaded from the open repository “Kaggle,” divided into 5,216 images for training, 16 for validation, and 624 for testing. Preprocessing involved image augmentation through modifications to the original images, scaling, and batch division in tensor format. A comparative analysis was conducted among the transfer models: DenseNet, VGG19, and ResNet50 version 2. Each transfer model was the header of a CNN with four subsequent layers. The models underwent training, validation, and testing phases. The test’s results showed that DenseNet achieved an accuracy of 0.87, VGG19 achieved 0.86, and ResNet50 achieved 0.91. These results affirm the effectiveness of ResNet50 in image classification, considering that the model’s output is binary, where 0 represents that the patient does not have pneumonia and 1 indicates that the patient has pneumonia.

Neurodegenerative disorders, notably Alzheimer’s, pose an escalating global health challenge. Marked by the degeneration of brain neurons, these conditions lead to a gradual decline in nerve cells. Worldwide, over 55 million people grapple with dementia, with Alzheimer’s prominently impacting the aging demographic. The primary hurdle to early Alzheimer’s detection is the widespread lack of awareness. The main goal is to design and implement an artificial intelligence system using deep learning (DL) to detect Alzheimer’s disease (AD) through medical images and classify them into various stages, such as non-demented, moderate dementia, mild dementia, and very mild dementia. The dataset contains 6400 magnetic resonance images in .jpg format, with standardized dimensions of 176 × 208 pixels. To demonstrate the advantages of data augmentation and transformation techniques, four scenarios were created: two without these techniques, utilizing the Adam and SGD optimizers, and two with these techniques, also employing the Adam and SGD optimizers, respectively. The main results revealed that scenarios utilizing these techniques exhibited more stable performance when validated with a new dataset. Scenario 3, using the Adam optimizer, achieved a weighted average accuracy of 91.83%, whereas scenario 4, employing the SGD optimizer, reached 87.58% accuracy. In contrast, scenarios 1 and 2, which omitted these techniques, obtained low accuracies below 55%. It is concluded that classifying AD with a DL model exceeding 90% accuracy is feasible. This is the importance of utilizing data augmentation and transformation techniques to improve generalizability to input image variations, which is a consistent factor in the healthcare sector.