A Comprehensive Survey on Brain Tumor Detection and Classification Techniques Using Machine Learning and Deep Learning Models

Abstract: Brain tumor detection and classification have become critical areas of research due to the complexities and challenges involved in accurate diagnosis. With the advancement of machine learning and deep learning techniques, numerous intelligent systems have been proposed to automate and enhance the process of detecting and classifying brain tumors. This survey paper reviews recent approaches and techniques used for brain tumor detection, focusing primarily on deep learning architectures like Convolutional Neural Networks (CNNs) and pre-trained models such as VGG, ResNet, and EfficientNet. In particular, methods using MRI images to classify tumors into categories such as glioma, meningioma, pituitary tumors, and healthy tissue are explored. A detailed analysis of various models, their architectures, training strategies, and evaluation metrics is provided. Moreover, the paper identifies the key challenges faced in the field, including imbalanced datasets, generalization issues, and computational efficiency. By comparing different approaches, we aim to highlight the strengths and limitations of each method and provide insights into how future research can address these challenges. The paper concludes with a discussion on promising future directions in the application of intelligent techniques for brain tumor detection, including the integration of multimodal data and the development of more interpretable models. This survey serves as a comprehensive resource for researchers interested in understanding the current landscape of brain tumor detection using advanced deep learning techniques.