Explainable Automated Brain Tumor Detection Using CNN

Brain tumors have been considered the world’s, most dangerous disease. Worldwide, approximately 0.25 million people die every year due to CNS tumors and primary cancerous brains. Histopathological examination of biopsy samples is still used in the diagnosis and classification of brain tumors. The existing method is obtrusive, tedious, and sensitive to errors by individuals. To overcome the pitfalls mentioned above for brain tumor multi-class classification, a fully automated deep learning system has been proposed for the early detection of brain tumors in an efficient way. For the purpose of early diagnosis, this research uses Convolutional Neural Networks (CNN) to multi-classify brain tumors. A heatmap image has been produced using the gradient-weighted class activation map (Grad-CAM) technique, and then from the heatmap, a bounded box image has been generated to demonstrate which regions of an image the proposed model devoted significantly more focus to than the other areas. This has been done to show that the proposed model is highly efficient. For the publicly accessible combined dataset, the proposed model attained testing accuracy, precision, recall, F1-score, and AUC scores of 98.75%, 98.00%, 98.00%, 97.76%, and 100%, respectively. The state-of-the-art (SOTA) methods for brain tumor multi-class classification has been outperformed by the proposed way after a number of hyperparameters have been tuned to yield the best outcomes. As a result, this model can help healthcare and radiology professionals check their first screening in order to categorize various kinds of brain tumors.