Brain Tumor Detection Using Deep Learning

This paper explores the application of deep learning in detecting brain tumors through the comparison of ResNet50 and VGG19 classification models. The study also includes the comparison of Res-U-Net and U-Net models for tumor segmentation. The process involves feeding an input of Brain MRI scan into the classification model, which provides two outputs: detection of the tumor or confirmation of the patient's health. If a tumor is detected, the segmentation model locates it at a pixel level. The proposed approach has the potential to improve the accuracy and speed of brain tumor diagnosis, which could lead to more effective treatment.