In medical imaging, the last decade has witnessed a remarkable increase in the availability
and diversity of chest X-ray (CXR) datasets. Concurrently, there has been a significant …

A crucial element in the diagnosis of breast cancer is the utilization of a classification method
that is efficient, lightweight, and precise. Convolutional neural networks (CNNs) have …

Medical imaging is considered a suitable alternative testing method for the detection of lung
diseases. Many researchers have been working to develop various detection methods that …

In the rapidly advancing field of biomedical engineering, effective real-time control of
artificial limbs is a pressing research concern. Addressing this, the current study introduces a …

Lung-related diseases continue to be a leading cause of global mortality. Timely and precise
diagnosis is crucial to save lives, but the availability of testing equipment remains a …

This paper presents a novel approach that merges a lightweight parallel depth-wise
separable convolutional neural network (LPDCNN) with a ridge regression extreme learning …

Predicting diseases in the early stages is extremely important. By taking advantage of
advances in deep learning and fuzzy logic techniques, a new model is proposed in this …

Background and Objective: Regarding the Coronavirus disease-2019 (COVID-19) pandemic
in past years and using medical images to detect it, the image processing of the lungs and …

Tuberculosis (TB) is a worldwide severe health concern that causes numerous deaths
yearly. Detecting TB promptly and precisely is crucial for limiting its effects and preventing …

Due to its high infectivity, COVID-19 has rapidly spread worldwide, emerging as one of the
most severe and urgent diseases faced by the global community in recent years. Currently …