The process of segmenting tumor from MRI image of a brain is one of the highly focused
areas in the community of medical science as MRI is noninvasive imaging. This paper …

Objective (1) To evaluate the diagnostic performance of radiomics in differentiating high-
grade glioma from brain metastasis and how to improve the model.(2) To assess the …

Background and objective Computer automated diagnosis of various skin lesions through
medical dermoscopy images remains a challenging task. Methods In this work, we propose …

Background Renal impairment is common in patients with coronary artery disease and, if
severe, late gadolinium enhancement (LGE) imaging for myocardial infarction (MI) …

This study aimed to identify the optimal radiomic machine-learning classifier for
differentiating glioblastoma (GBM) from solitary brain metastases (MET) preoperatively. Four …

Introduction Solitary brain metastasis (MET) and glioblastoma multiforme (GBM) can appear
similar on conventional MRI. The purpose of this study was to identify magnetic resonance …

The existing deep convolutional neural networks (DCNNs) based methods have achieved
significant progress regarding automatic glioma segmentation in magnetic resonance …

Objective. The purpose of this study was to investigate the feasibility of applying handcrafted
radiomics (HCR) and deep learning‐based radiomics (DLR) for the accurate preoperative …

Simple Summary We present a systematic review of published reports on machine learning
(ML) applications for the differentiation of gliomas from brain metastases by summarizing …

Purpose While MRI is the modality of choice for the assessment of patients with brain
tumors, differentiation between various tumors based on their imaging characteristics might …