An Optimized CNN Method for Multi-Class MRI Brain Tumor Classification

Multi-Class MRI Brain Tumor Classification is essential for lowering mortality rates in the field of medical imaging. Nevertheless, current techniques do not distinguish between various tumor forms, including glioma, meningioma, no-tumor, and pituitary tumors. Hyper-parameters like batch size and epochs are not optimized by these approaches. As a result, classification accuracy is affected. This paper provides a uniquely optimized CNN method that combines the pre-trained AlexNet CNN model with the Rat Swarm Optimizer technique in order to overcome the aforementioned constraints. There are three stages to this process. First, images of MRI brain tumors are acquired from the BraTS2020 database, which contains images of pituitary, glioma, meningioma, and no-tumor. Second, the images were classified using the pre-trained AlexNet CNN model. Finally, the hyper-parameters like as batch size and epochs are optimized using a rat swarm optimizer to obtain the best values for improved classification. The effectiveness of the suggested technique is evaluated using the F1-score, sensitivity, specificity, and accuracy metrics. When compared to current methods, the proposed approach performs better.