Coronary Artery Disease (CAD) occurs when the coronary vessels become hardened and narrowed, limiting blood flow to the heart muscles. It is the most common type of heart disease and has the highest mortality rate. Early diagnosis of CAD can prevent the disease from progressing and can make treatment easier. Optimal treatment, in addition to the early detection of CAD, can improve the prognosis for these patients. This study proposes a new method for non-invasive diagnosis of CAD using iris images. In this study, iridology, a method of analyzing the iris to diagnose health conditions, was combined with image processing techniques to detect the disease in a total of 198 volunteers, 94 with CAD and 104 without. The iris was transformed into a rectangular format using the integral differential operator and the rubber sheet methods, and the heart region was cropped according to the iris map. Features were extracted using wavelet transform, first-order statistical analysis, a Gray-Level Co-Occurrence Matrix (GLCM), and a Gray Level Run Length Matrix (GLRLM). The model’s performance was evaluated based on accuracy, sensitivity, specificity, precision, score, mean, and Area Under the Curve (AUC) metrics. The proposed model has a 93% accuracy rate for predicting CAD using the Support Vector Machine (SVM) classifier. With the proposed method, coronary artery disease can be preliminarily diagnosed by iris analysis without needing electrocardiography, echocardiography, and effort tests. Additionally, the proposed method can be easily used to support telediagnosis applications for coronary artery disease in integrated telemedicine systems.

Background/Objectives: Alzheimer’s disease (AD) is a devastating neurodegenerative disorder that progressively impairs cognitive, neurological, and behavioral functions, severely affecting quality of life. The current diagnostic process relies on expert interpretation of extensive clinical assessments, often leading to delays that reduce the effectiveness of early interventions. Given the lack of a definitive cure, accelerating and improving diagnosis is critical to slowing disease progression. Electroencephalography (EEG), a widely used non-invasive technique, captures AD-related brain activity alterations, yet extracting meaningful features from EEG signals remains a significant challenge. This study introduces a machine learning (ML)-driven approach to enhance AD diagnosis using EEG data. Methods: EEG recordings from 36 AD patients, 23 Frontotemporal Dementia (FTD) patients, and 29 healthy individuals (HC) were analyzed. EEG signals were processed within the 0.5–45 Hz frequency range using the Welch method to compute the Power Spectral Density (PSD). From both the time-domain signals and the corresponding PSD, a total of 342 statistical and spectral features were extracted. The resulting feature set was then partitioned into training and test datasets while preserving the distribution of class labels. Feature selection was performed on the training set using Spearman and Pearson correlation analyses to identify the most informative features. To enhance classification performance, hyperparameter tuning was conducted using Bayesian optimization. Subsequently, classification was carried out using Support Vector Machines (SVMs) and k-Nearest Neighbors (k-NN) the optimized hyperparameters. Results: The SVM classifier achieved a notable accuracy of 96.01%, outperforming previously reported methods. Conclusions: These results demonstrate the potential of machine learning-based EEG analysis as an effective approach for the early diagnosis of Alzheimer’s Disease, enabling timely clinical intervention and ultimately contributing to improved patient outcomes.

Background/Objectives: This paper is significant in highlighting the importance of early and precise diagnosis of Parkinson’s Disease (PD) that affects both motor and non-motor functions to achieve better disease control and patient outcomes. This study seeks to assess the effectiveness of machine learning algorithms optimized to classify PD based on vocal characteristics to serve as a non-invasive and easily accessible diagnostic tool. Methods: This study used a publicly available dataset of vocal samples from 188 people with PD and 64 controls. Acoustic features like baseline characteristics, time-frequency components, Mel Frequency Cepstral Coefficients (MFCCs), and wavelet transform-based metrics were extracted and analyzed. The Chi-Square test was used for feature selection to determine the most important attributes that enhanced the accuracy of the classification. Six different machine learning classifiers, namely SVM, k-NN, DT, NN, Ensemble and Stacking models, were developed and optimized via Bayesian Optimization (BO), Grid Search (GS) and Random Search (RS). Accuracy, precision, recall, F1-score and AUC-ROC were used for evaluation. Results: It has been found that Stacking models, especially those fine-tuned via Grid Search, yielded the best performance with 92.07% accuracy and an F1-score of 0.95. In addition to that, the choice of relevant vocal features, in conjunction with the Chi-Square feature selection method, greatly enhanced the computational efficiency and classification performance. Conclusions: This study highlights the potential of combining advanced feature selection techniques with hyperparameter optimization strategies to enhance machine learning-based PD diagnosis using vocal characteristics. Ensemble models proved particularly effective in handling complex datasets, demonstrating robust diagnostic performance. Future research may focus on deep learning approaches and temporal feature integration to further improve diagnostic accuracy and scalability for clinical applications.

Background/Objectives: Early diagnosis of Parkinson’s Disease (PD) is essential for initiating interventions that may slow its progression and enhance patient quality of life. Gait analysis provides a non-invasive means of capturing subtle motor disturbances, enabling the prediction of both disease presence and severity. This study evaluates and contrasts Bayesian-optimized convolutional neural network (CNN) and long short-term memory (LSTM) models applied directly to Vertical Ground Reaction Force (VGRF) signals for Parkinson’s disease detection and staging. Methods: VGRF recordings were segmented into fixed-length windows of 5, 10, 15, 20, and 25 s. Each segment was normalized and supplied as input to CNN and LSTM network. Hyperparameters for both architectures were optimized via Bayesian optimization using five-fold cross-validation. Results: The Bayesian-optimized LSTM achieved a peak binary classification accuracy of 99.42% with an AUC of 1.000 for PD versus control at the 10-s window, and 98.24% accuracy with an AUC of 0.999 for Hoehn–Yahr (HY) staging at the 5-s window. The CNN model reached up to 98.46% accuracy (AUC = 0.998) for binary classification and 96.62% accuracy (AUC = 0.998) for multi-class severity assessment. Conclusions: Bayesian-optimized CNN and LSTM models trained on VGRF data both achieved high accuracy in Parkinson’s disease detection and staging, with the LSTM exhibiting a slight edge in capturing temporal patterns while the CNN delivered comparable performance with reduced computational demands. These results underscore the promise of end-to-end deep learning for non-invasive, gait-based assessment in Parkinson’s disease.

Tr en Görüntü içerisindeki nesnelerin tespit edilmesi ve sınıflandırılma uygulamaları her geçen gün artmaktadır. Bu çalışmada da robotik uygulamalarda da kullanılabilecek bir nesne tespiti ve sınıflandırılması uygulaması gerçekleştirilmiştir. Alexnet Evrişimsel Sinir Ağları (ESA) mimarisi ve Bölgesel Evrişimsel Sinir Ağları (B-ESA) algoritması ile gerçekleştirilen çalışmada yedi farklı nesne sınıfı seçilmiştir. Veri setindeki 684 eğitim verisi etiketlenerek ağın eğitilmesinde kullanılmıştır. 226 test görüntüsü eğitilen ağda test edilmesi sonucunda her sınıfa ait doğru tahmin değerleri ve toplam doğruluk değerleri bulunmuştur. Sınıflara ait tahminlerde en düşük %85,74 ve en yüksek %100 değerlerine ulaşılmıştır. Tüm test verileri için doğruluk değeri %93,81 bulunmuştur. The detection and classification applications of the objects in the image are increasing day by day. In this study, an object detection and classification application, which can also be used in robotic applications, has been realized. Seven different object classes were selected in the study conducted with Alexnet Evolutionary Neural Networks (CNN) architecture and Regional Convolutional Neural Networks (R-CNN) algorithm. 684 training data in the data set were labelled and used to train the network. As a result of testing 226 test images in the trained network, correct predictive values and total accuracy values of each class were found. The lowest estimates of 85.74% and the highest 100% were reached in the estimates of the classes. The accuracy value was 93.81% for all test data.

Radiographic imaging is a crucial tool frequently employed by dentists for initial diagnosis and treatment planning. However, these images often suffer from distortion or inaccuracies due to incorrect exposure settings, making it challenging to identify critical regions such as tooth roots and margins. This study addresses these issues by presenting two innovative methods for tooth segmentation from radiographs, aimed at isolating the tooth regions for better analysis. The first method utilizes fuzzy logic rules to detect edges within the radiographic images. These detected edges are then used as a mask for the Active Contour Method (ACM) to segment the teeth accurately. The second method involves the creation of a Convolutional Neural Network (CNN) for tooth segmentation. The segmentation performance of the CNN is further refined using the ACM, leveraging the initial segmentation as a mask. Both methods demonstrated notable results with varying performance metrics. Specifically, the Fuzzy-Based Active Contour Method achieved precision, recall, and F1 score values of 0.6246, 0.4169, and 0.50, respectively. In contrast, the CNN-Based Active Contour Method calculated accuracy and specificity values of 0.9706 and 0.9872, respectively. These findings indicate that both approaches have distinct strengths in different performance aspects. Our study suggests that these advanced segmentation techniques can significantly enhance the diagnostic capabilities of dental professionals by providing clearer images of tooth structures, aiding in the detection of issues such as root problems, fractures, and wear patterns. Implementing these methods either independently or in combination could lead to more accurate diagnoses and better patient outcomes. Future work could explore the integration of these techniques to leverage their complementary strengths, potentially leading to even greater segmentation accuracy and reliability.