Multi-label learning is a rapidly growing research area that aims to predict multiple labels
from a single input data point. In the era of big data, tasks involving multi-label classification …

Lung cancer is relying as one of the significant and leading cause for the deaths which are
based on cancer. So, an effective diagnosis is a crucial step to save the patients who are all …

Nowadays, artificial intelligence (AI) has been utilized in several domains of the healthcare
sector. Despite its effectiveness in healthcare settings, its massive adoption remains limited …

Classification performance often deteriorates when machine learning algorithms are trained
on imbalanced data. Although oversampling methods have been successfully employed to …

Multi-label classification is an advantageous technique for managing uncertainty in
classification problems where each data instance is associated with several labels …

Chest X-ray scans are one of the most often used diagnostic tools for identifying chest
diseases. However, identifying diseases in X-ray images needs experienced technicians …

In the realm of medical diagnostics, particularly in differential diagnosis, where differentiating
between illnesses or ailments with comparable symptoms is essential, deep learning has …

Biomedical knowledge reasoning is required to enhance the explainability of medical AI
applications by overcoming the limitations of deep-learning methods. This research area …

In order to supply the world's protein needs, one of the most crucial industries is the poultry
business. The problem that often occurs in chicken farms is disease, and this can have a …

Medical image datasets, particularly those comprising Magnetic Resonance (MR) images,
are essential for accurate diagnosis and treatment planning. However, these datasets often …