Integrating AI for Improved Brain Tumor Detection and Classification

Brain cancer is one of the greatest health difficulties owing to the various and intricate structures. It is very important to identify them at an early stage and classify them correctly to enhance patients' outcomes and monitor clinical decisions. This proposed work aims to develops an effective method of improving various aspects of brain tumor detection coupled with its classification mechanism through artificial intelligence. This research work has three modules such as Brain tumor detection, segmentation and tumor classification. Algorithms and sophisticated microscopes are used for crafting a dependable method of diagnosing without the invasion of the patient's body. The approach especially involves Convolutional Neural Networks (CNNs) applied on large and varied brain tumor medical image datasets to classify various types of brain tumors with considerable accuracy. The proposed system utilizes brain MRI Brain Tumor dataset. Such strategies as data augmentation and transfer learning are performed in order to enhance both, model reliability and its performance. The brain tumor classification system provides higher accuracy compared to other classification techniques.