Advancing chest X-ray diagnostics: A novel CycleGAN-based preprocessing approach for enhanced lung disease classification in ChestX-Ray14.

Background And Objective: Chest radiography is a medical imaging technique widely used to diagnose thoracic diseases. However, X-ray images may contain artifacts such as irrelevant objects, medical devices, wires and electrodes that can introduce unnecessary noise, making difficult the distinction of relevant anatomical structures, and hindering accurate diagnoses. We aim in this study to address the issue of these artifacts in order to improve lung diseases classification results.

Lung and colon cancer classification using medical imaging: a feature engineering approach.

Lung and colon cancers lead to a significant portion of deaths. Their simultaneous occurrence is uncommon, however, in the absence of early diagnosis, the metastasis of cancer cells is very high between these two organs. Currently, histopathological diagnosis and appropriate treatment are the only way to improve the chances of survival and reduce cancer mortality.

Overview on prediction, detection, and classification of atrial fibrillation using wavelets and AI on ECG.

Atrial fibrillation (AF) is the most common supraventricular cardiac arrhythmia, resulting in high mortality rates among affected patients. AF occurs as episodes coming from irregular excitations of the ventricles that affect the functionality of the heart and can increase the risk of stroke and heart attack. Early and automatic prediction, detection, and classification of AF are important steps for effective treatment.