Background The purpose of this study is to investigate the use of radiomics and deep
features obtained from multiparametric magnetic resonance imaging (mpMRI) for grading …

Objective Radiomic and deep learning studies based on magnetic resonance imaging (MRI)
of liver tumor are gradually increasing. Manual segmentation of normal hepatic tissue and …

Kidney tumors are a significant health concern. Early detection and accurate segmentation
of kidney tumors are crucial for timely and effective treatment, which can improve patient …

Objective The goal of this study was to evaluate the efficacy of machine learning (ML)
techniques in predicting survival for chordoma patients in comparison with the standard Cox …

This study investigates the classification of Arabic coffee into three major variations (light,
medium, and dark) using simulated data gathered from the actual measurements of color …

Different machine learning algorithms have different characteristics and applicability. This
study aims to predict ruptured intracranial aneurysms by radiomics models based on …

Background The machine learning models with dose factors and the deep learning models
with dose distribution matrix have been used to building lung toxics models for radiotherapy …

Abstract Background Prostate cancer (PCa) is one of the most common cancers in men
worldwide, and its timely diagnosis and treatment are becoming increasingly important. MRI …

This study compared the diagnostic efficiency of benign and malignant breast nodules using
ultrasonographic characteristics coupled with several machine-learning models, including …

Background Magnetic resonance imaging (MRI) plays an increasingly important role in
radiotherapy, enhancing the accuracy of target and organs at risk delineation, but the …