Brain tumor detection and classification using CNN and resnet-50

Among the largest problems facing medication is finding and regarding the categorization of the brain tumors. To solve this, a method that leverages Artificial Intelligence and device learning is advised. This includes getting and processing neurological imaging data, separating growths by utilizing Deep Learning techniques, and obtaining radiomic features for classification. For classification, a Models of CNNs are used, and demanding approval and cross-validations techniques are used. To upgrade the selection procedure, the interpretability of the example is addressed. The method has its basis in ethical principles, including data about patients' privacy alongside conformity to regulations, and it is technically relevant. This work develops the brain tumor identification as a topic and categorization and demonstrates the worth of savvy arrives at in medical treatment. The proposed approach lays the groundwork for future developments and AI-driven solutions in clinical settings, benefiting patients and medical professionals.