Simple Summary This study investigates the latest achievements, challenges, and future
research directions of deep learning techniques for lung cancer and pulmonary nodule …

The integration of artificial intelligence (AI) into medical imaging has guided in an era of
transformation in healthcare. This literature review explores the latest innovations and …

Simple Summary This study provides an efficient method for lung cancer diagnosis from
computed tomography images and employs deep learning-supported support vector …

Lung Cancer is one of the deadliest cancers, responsible for more than 1.80 million deaths
annually worldwide, and it is on the priority list of WHO. In the current scenario, when cancer …

Rationale and Objectives Accurate staging of laryngeal carcinoma can inform appropriate
treatment decision-making. We developed a radiomics model, a deep learning (DL) model …

There is an increasing need for inexpensive and rapid screening tests in point-of-care
clinical oncology settings. Herein, we develop a swab “dip” test in saliva obtained from …

Recently, assistive explanations for difficulties in the health check area have been made
viable thanks in considerable portion to technologies like deep learning and machine …

Purpose To evaluate the feasibility of leveraging serial low-dose CT (LDCT) scans to
develop a radiomics-based reinforcement learning (RRL) model for improving early …

This study summarizes the latest achievements, challenges, and future research directions
in deep learning technologies for the diagnosis of renal cell carcinoma (RCC). This is the …

Purpose The objective of this study was to create and validate a machine learning (ML)-
based model for predicting the likelihood of lung infections following chemotherapy in …