This article is mainly concerned with COVID‐19 diagnosis from X‐ray images. The number of cases infected with COVID‐19 is increasing daily, and there is a limitation in the number of test kits needed in hospitals. Therefore, there is an imperative need to implement an efficient automatic diagnosis system to alleviate COVID‐19 spreading among people. This article presents a discussion of the utilization of convolutional neural network (CNN) models with different learning strategies for automatic COVID‐19 diagnosis. First, we consider the CNN‐based transfer learning approach for automatic diagnosis of COVID‐19 from X‐ray images with different training and testing ratios. Different pre‐trained deep learning models in addition to a transfer learning model are considered and compared for the task of COVID‐19 detection from X‐ray images. Confusion matrices of these studied models are presented and analyzed. Considering the performance results obtained, ResNet models (ResNet18, ResNet50, and ResNet101) provide the highest classification accuracy on the two considered datasets with different training and testing ratios, namely 80/20, 70/30, 60/40, and 50/50. The accuracies obtained using the first dataset with 70/30 training and testing ratio are 97.67%, 98.81%, and 100% for ResNet18, ResNet50, and ResNet101, respectively. For the second dataset, the reported accuracies are 99%, 99.12%, and 99.29% for ResNet18, ResNet50, and ResNet101, respectively. The second approach is the training of a proposed CNN model from scratch. The results confirm that training of the CNN from scratch can lead to the identification of the signs of COVID‐19 disease.

Introduction: In the domain of women’s health, the intricate conditions of Polycystic Ovary Syndrome (PCOS) demand sophisticated methodologies for accurate identification and intervention.Methods: This study introduces an innovative machine learning framework tailored to precisely classify instances of PCOS. The methodology incorporates stacked learning and depends on the Adaptive Synthetic (ADASYN) algorithm, Synthetic Minority Over-sampling Technique (SMOTE), and random oversampling methods for addressing data imbalances. The BORUTA technique is used for feature selection, with the overarching objective of advancing precision and performance metrics in classification tasks.Results: Within the scope of PCOS classification, the proposed framework achieves a commendable 97% accuracy. These results underscore the proficiency of the proposed framework in discriminating PCOS cases with a high degree of precision. Critical to this contribution is the rigorous comparative analysis against existing methodologies, affirming the superior accuracy and performance attributes of the proposed framework.Discussion: This substantiates its potential as a transformative tool in medical classification. Moreover, beyond immediate applications, this paper explores the generalization of the proposed framework, demonstrating its adaptability and efficacy across different medical classifications. This versatility is exemplified by its successful application to cervical cancer, showcasing the framework potential as a pioneering force in reshaping the landscape of machine-learning applications in healthcare diagnostics.