MRI Brain Tumor Segmentation and Classification using different deep learning models

Brain tumors have significant challenges in diagnostic precision due to their complex morphological subtleties evident in MRI (Magnetic Resonance Imaging) scans. Clinical Decision Support Systems (CDSS) have become indispensable in this regard, with MRI brain tumor segmentation and classification at their forefront. This comprehensive survey deals with the profound impact of deep learning techniques on MRI brain tumor analysis, particularly within the context of CDSS. Emphasizing the models validated against the various Dataset, the exploration ranges from the intricate convolutional processes of CNNs (Convolutional Neural Networks) to the advanced self-attention mechanisms offered by recent Transformer-based methodologies. The significance of 3D datasets is underscored, given their ability to offer detailed and holistic representations of brain tumors. Metrics such as DSC (Dice Similarity Coefficient), JI (Jaccard Index), and sensitivity rates serve as pivotal benchmarks to evaluate the efficiency of these deep learning models. Moreover, this survey investigates the performance trade-offs inherent within these methodologies, pointing towards potential avenues for further enhancement and adaptability. By synthesizing this expansive body of knowledge, the goal is to harmonize cutting- edge technological strides with tangible clinical utility, thereby refining diagnostic accuracy and forging a brighter traj ectory for patient care in the realm of brain tumor studies.