BrainNet: A Deep Learning Approach for Brain Tumor Classification

Abstract

Cancer, regardless of its type, represents a formidable threat to human life and disrupts the delicate balance of normal bodily functions. Among the various forms of cancer, malignant brain tumors stand out as a leading cause of mortality in both adult and pediatric populations. The timely identification of these brain tumors is crucial for attaining precise diagnoses. Brain tumour identification and diagnosis are now made possible by the use of magnetic resonance imaging (MRI). However, the intricate and irregular shapes and locations of these tumors often pose challenges for complete comprehension. Typically, the expertise of neurosurgical specialists is required for the precise analysis of MRI scans. Unfortunately, in many developing countries, a shortage of skilled medical professionals and limited awareness about brain tumors compound the difficulties associated with obtaining timely and accurate MRI results. To address these notable challenges, this research introduces BrainNet, an innovative Convolutional Neural Network (CNN) architecture specifically designed for the classification of brain tumors into distinct categories. The established transfer learning models VGG13, VGG19, VGG16, InceptionResV2, and Squeeznet, all of which were pretrained on the Imagenet dataset, are outperformed by BrainNet in both how well it handles these problems and how well it outperforms them. The performance of the BrainNet CNN architecture is particularly impressive, with a precision score of 94.75 percent and accuracy rates of 99.96 percent during training and 97.71 percent during testing. This accomplishment has the potential to significantly improve brain tumour diagnosis and classification, particularly in areas with limited access to medical resources and knowledge.