Brain Tumor Segmentation and Classification Using Deep Learning

Early identification is essential for the effective treatment of brain tumors, which pose a serious health risk. In this study, a novel neural network technique for brain tumor identification is presented. However, time-consuming and error-prone manual segmentation and analysis by radiologists are common in conventional methods for brain tumor detection. Magnetic resonance imaging (MRI) scans and other medical images can be used to train neural networks to recognize brain tumors automatically. According to certain research, CNNs can identify brain tumors with accuracies of over 99%. Neural networks can automate the process of finding brain tumors, increasing the precision and effectiveness of diagnosis, and eventually, improving patient outcomes. Convolutional neural networks (CNNs) are used in the proposed system and are trained on a substantial dataset of labeled brain scans, enabling it to discover intricate patterns and variations linked to various tumor kinds.