Investigating Brain Tumor Detection and Classification through various Deep Learning Approaches

Classification and Detection of brain tumors is a significant phase to better understanding its appliance. MRI and CT scan images are used for experiment analysis and medical imaging method which helps the radiologists explore the tumor area. Moreover, it is time time-consuming procedure and needs expertise to analyze the MRI and CT images, manually. Recent advances, the new advancements in CAD, ML, and DL in particular enabled radiologists to more dependably identify brain tumors. The new approaches utilized to handle this need a hand-crafted feature for classification reasons. Whereas DL approaches can be developed to perform classification without any requirement for handcrafted features. This article compares the analysis of three different DL methods to classify brain tumors. Various brain tumor dataset categories, segmentation methods, and detection strategies are also presented. The 23-layered CNN model overtakes the AlexNet, and GoogleNet, models in terms of detection accuracy rate performance by 97.8%.