Deep learning for multi-grade brain tumor detection and classification: a prospective survey.

Brain tumors (BT) pose a significant threat to human life, making early detection and classification are critical for effective treatment. Medical imaging plays an important role in brain tumor detection (BTD) by providing invaluable data for diagnosis, surgical planning, research, and training. Maximum accuracy is essential when detecting brain tumors, as even minor diagnostic errors can have serious consequences. Detecting tumors in brain images is inherently complex due to various noise factors that affect image accuracy. This survey examines the significant contributions of deep learning (DL) in the analysis of Magnetic Resonance Imaging (MRI) medical images for brain tumor detection. MRI is the preferred diagnostic tool for examining the brain's intricate structures. DL has emerged as a powerful tool for medical image analysis and brain tumor detection, offering promising solutions to these challenges. This survey paper focuses on the application of DL techniques in the analysis of MRI medical images for brain tumor detection and classification. It presents an overview of diverse DL-based approaches for brain tumor classification (BTC) and their potential to assist radiologists in enhancing research and analysis. This paper reviews recent advancements in DL-based brain tumor detection, addressing current challenges and future prospects in this essential field. Additionally, this survey explores the use of standard datasets in the feature extraction (FE) and classification stages. This review provides a comprehensive analysis of recent developments that address the existing challenges in brain tumor detection using DL approaches. [ABSTRACT FROM AUTHOR]