Background: Knee osteoarthritis (KOA) is characterized by cartilage degradation and joint-space narrowing, resulting in increased friction and observable structural damage. Methods: This study introduces a composite hybrid framework for the automatic classification of KOA severity using anteroposterior knee X-ray images. The methodology applies joint-centered cropping and data augmentation to standardize inputs and uses class weighting to mitigate class imbalance. Deep features extracted from fine-tuned ResNet-101 and EfficientNetB7 models are integrated with handcrafted Gray Level Co-occurrence Matrix (GLCM) texture descriptors, and the final predictions are obtained using a soft-voting ensemble. Results: the proposed ensemble achieves 73% test accuracy (macro-F1 ≈ 0.70; weighted-F1 ≈ 0.73) in a four-class setting (KL-0, KL-2, KL-3, and KL-4). Additional experiments across different classification setups demonstrate consistent performance trends, while Grad-CAM indicates that the model primarily focuses on the joint region. Overall, Conclusions: combining ensemble deep learning with complementary handcrafted texture features provides a reliable and interpretable approach for grading radiographic KOA severity.

Early and reliable detection of cardiac disease is crucial for preventing complications and enhancing patient outcomes. Phonocardiogram (PCG) signals, which encode rich information about cardiac function, offer a non-invasive and cost-effective way to identify abnormalities such as valvular disorders, arrhythmias, and other heart pathologies. This study investigates advanced diagnostic methods for heart sound analysis to improve the detection and classification of cardiac abnormalities. In the proposed framework, recurrence plots (RPs) are used for feature extraction, while machine learning algorithms are applied for classification, creating a diagnostic model that can recognize cardiac conditions from composite acoustic signals. This method serves as an efficient alternative to more computationally intensive deep learning methods and other high-dimensional ML-based solutions. Experimental results demonstrate that the multiclass classification task achieves up to 98.4% accuracy, and the binary classification reaches 99.5% accuracy using 2 s signal segments. The techniques assessed in this research demonstrate the potential of automated heart sound analysis as a screening tool in both clinical and remote healthcare settings. Overall, the findings highlight the significance of machine learning in heart sound classification and its potential to facilitate timely, accessible, and cost-effective cardiovascular care.

Fetal hypoxia is a condition that is caused by insufficient oxygen supply to the fetus and poses serious risks, including abnormalities, birth defects, and potential mortality. Cardiotocography (CTG) monitoring is commonly used to identify fetal distress, including hypoxia, by categorizing cases as normal or hypoxia. However, traditional CTG interpretation, usually performed visually by experts, can be subjective and error-prone, resulting in observer variability and inconsistent outcomes. It highlights the need for an automated and objective diagnostic system to assist clinicians in interpreting CTG data more accurately and consistently. In this research, a fetal hypoxia diagnosis system is proposed based on CTG signals. The CTG dataset is first transformed into the time-frequency domain using instantaneous frequency and using common spatial pattern (CSP) for feature extraction. Finally, the extracted features are then used to train and evaluate four machine learning models for classification with a cross-validation 5-fold methodology. Objective criteria (pH values, BDecf, Apgar 1, and Apgar 5) and expert voting as a subjective criterion were used to classify the fetus as normal or hypoxia. The SVM model outperformed other models in detecting fetal hypoxia, achieving high accuracy across pH, BDecf, Apgar1, Apgar5, and expert voting in all steps. It achieved over 98% accuracy across all objective criteria and steps.