Work

Advancements in Radiomics and Deep Learning for Glioma Diagnosis and Characterization: a Review of Recent Studies

Year: 2023

Type: article

Abstract: Gliomas are the most common primary brain tumors, with varying degrees of malignancy and heterogeneity, making their diagnosis and characterization challenging. Radiomics, a rapidly emerging field, involves the extraction of a large number of quantitative imaging features from radiological images, which can provide valuable information for tumor diagnosis and treatment planning. Deep learning, a type of machine learning, has shown great potential in image recognition and classification tasks, and its integration with radiomics has led to significant advancements in glioma diagnosis and characterization.

Source: IBRO Neuroscience Reports

DOI: 10.1016/j.ibneur.2023.08.1183

Authors: Gandhi Bhaskar Patrudu Lanka, Abhijit Verma

Institution: NIMS University

Language: English

Topic: Radiomics and Machine Learning in Medical Imaging

Subfield: Radiology, Nuclear Medicine and Imaging

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $1,500