Background Transfer learning (TL) with convolutional neural networks aims to improve
performances on a new task by leveraging the knowledge of similar tasks learned in …

Background Deep learning offers considerable promise for medical diagnostics. We aimed
to evaluate the diagnostic accuracy of deep learning algorithms versus health-care …

Convolutional neural networks, are one of the most representative deep learning models.
CNNs were extensively used in many aspects of medical image analysis, allowing for great …

Since 2010, substantial progress has been made in artificial intelligence (AI) and its
application to medicine. AI is explored in gastroenterology for endoscopic analysis of …

Background and Aims The prognosis of esophageal cancer is relatively poor. Patients are
usually diagnosed at an advanced stage when it is often too late for effective treatment …

This paper aims to assist in the prevention of Chronic Kidney Disease (CKD) by utilizing
machine learning techniques to diagnose CKD at an early stage. Kidney diseases are …

Background and Aims Although erosions and ulcerations are the most common small-bowel
abnormalities found on wireless capsule endoscopy (WCE), a computer-aided detection …

Artificial intelligence (AI) using deep-learning (DL) has emerged as a breakthrough
computer technology. By the era of big data, the accumulation of an enormous number of …

Purpose The aim of this systematic review was to analyse literature on artificial intelligence
(AI) and radiomics, including all medical imaging modalities, for oncological and non …

The development of artificial intelligence (AI) has increased dramatically in the last 20 years,
with clinical applications progressively being explored for most of the medical specialties …