Brain tumor detection, classification and segmentation by deep learning models from MRI images: Recent approaches, challenges and future directions

The diagnosis of brain tumors presents a very important challenge in the neuro-oncology field because of the complexities, heterogeneity, and high mortality of the tumors. The latest trends in deep learning have revolutionized the research in the field of medical image analysis. Through these trends, automated and precise brain tumor-detection, classification, and segmentation have been accomplished. This paper provides a systematic review with the focus taxonomy of the development of brain tumor analysis models with the most advanced deep neural networks based on tasks, and methods. It also summarizes the information of commonly available public datasets, preprocessing methods for MRI images, performance evaluation metrics. It reviews in detail on deep learning models available for brain tumor detection,classification, and segmentation in terms of performance metrics and clinical relevances. For representing in organized way, all the works reviewed here are divided into several groups and compared on specific benchmarks. Moreover, it figures out current challenges regarding brain tumor diagnosis and the potential implications of future studies to increase clinical applicability and trustworthiness of AI-driven solutions. This review acts as an informational guide to any researchers and healthcare professionals. It describes recent emerging patterns, current issues, and opportunities of deep learning to transform the diagnosis and treatment of brain tumors.