Deep Learning Approaches for Brain Tumor Detection in MRI Images: A Comprehensive Survey - Jadhav, Rahul Namdeo; Sudhaghar, G. (International Journal of Intelligent Systems and Applications in Engineering, 2024)
Deep learning techniques are in constant evolution. This rapid growth is clearly visible in the arena of medical imaging, particularly in the detection of brain tumors through MRI scans. Our thorough review outlines the range of data sets involved in tumor detection. We elaborate on multiple deep-learning procedures deployed for this purpose, primarily spotlighting key frameworks such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). With references to prior studies, we discern trends in model performance and their potential influence within health care settings. This review dives deep into learning methods in a comprehensive manner. It also addresses the continual struggle of insufficient labeled data for training robust models. Additionally, we discuss the advantages of data augmentation, normalization, and standardization in preprocessing. Comparisons of performance assessment metrics, including sensitivity, specificity, accuracy, recall, AUC-ROC, and F1 score, offer a clearer understanding of model efficiency. Our review’s strength lies in its exhaustive outlook of the current scenario in brain tumor detection, presenting valuable observations for researchers and practitioners alike. We discuss multiple methods and data sets while foretelling potential trends and future shifts, like utilizing various modes and increasing demand for explainable AI in medical imaging. This paper collates prevalent wisdom and serves as a progressive guide for deep learning-based research in brain tumor detection, contributing to the continuous enhancement of diagnostic tools employed clinically.