1 Abstract Problem Machine learning (ML)/Deep learning (DL) techniques have been
evolving to solve more complex diseases, but it has been used relatively little in …

Background Deep learning has revolutionized digital pathology, allowing automatic analysis
of hematoxylin and eosin (H&E) stained whole slide images (WSIs) for diverse tasks. WSIs …

Systemic lupus erythematosus (SLE) is an autoimmune inflammatory disease. Lupus
nephritis (LN) is a major risk factor for morbidity and mortality in SLE. Proliferative and pure …

Tuberculous pleural effusion poses a significant threat to human health due to its potential
for severe disease and mortality. Without timely treatment, it may lead to fatal consequences …

Unsupervised feature selection (UFS) takes an important position because gaining the class
labels is laborious or even impossible. In the domain of UFS, clustering is a major means to …

The recent success of deep learning coincides with the surge of digital pathology as an
effect of the pandemic. However, processing a massive volume of gigapixel histopathology …

Melanoma, a widespread and hazardous form of cancer, has prompted researchers to
prioritize dermoscopic image‐based algorithms for classifying skin lesions. Recently, there …

Feature selection is a crucial step in machine learning, especially for high-dimensional
datasets, where irrelevant and redundant features can degrade model performance and …

Parkinson's disease is accepted as one of the most important diseases in the world.
Parkinson's disease can be diagnosed in various conventional techniques. Recently, these …

Abstract Machine learning (ML) is increasingly applied for medical image processing with
appropriate learning paradigms. These applications include analyzing images of various …