Accurate prediction of the turnover number (k), which quantifies the maximum rate of substrate conversion at an enzyme's active site, is essential for assessing catalytic efficiency and understanding biochemical reaction mechanisms. Traditional wet-l...
One of the main risk factors for numerous health problems is excessive drinking. Alcoholism is a severe disorder that can affect a person's thinking and cognitive abilities. Early detection of alcoholism can help the subject regain control over their...
The medical community continually seeks innovative solutions to address healthcare challenges, particularly in cancer detection. A promising approach involves the use of Artificial Intelligence (AI) techniques, specifically Deep Learning (DL) models....
Mosquito-borne diseases, such as Yellow fever, Dengue, and Zika, pose a significant global health threat, causing millions of deaths annually. Traditional mosquito identification methods, reliant on expert analysis, are time-consuming and resource-in...
The challenge of precisely recognizing myocardial infarction (MI) from electrocardiographic (ECG) readings stems from the complex nature of these signals.ECG data exhibit both nonlinear and non-stationary properties, making interpretation difficult. ...
BACKGROUND: Grading fluorescein angiography (FA) for uveitis is complex, often leading to the oversight of retinal inflammation in clinical studies. This study aims to develop an automated method for grading retinal inflammation.
Breast cancer remains a leading cause of mortality among women worldwide, underscoring the need for accurate and timely diagnostic methods. Precise segmentation of nuclei in breast histopathology images is crucial for effective diagnosis and prognosi...
This study systematically examines the impact of training database size and the generalizability of deep learning models for synthetic medical image generation. Specifically, we employ a Cycle-Consistency Generative Adversarial Network (CycleGAN) wit...