Brain tumor MRI Classification using a Novel Deep Residual and Regional CNN

Abstract Brain tumor classification is crucial for clinical analysis and an effective treatment plan to cure patients. Deep learning models help radiologists to accurately and efficiently analyze tumors without manual intervention. However, brain tumor analysis is challenging because of its complex structure, texture, size, location, and appearance. Therefore, a novel deep residual and regional-based Res-BRNet Convolutional Neural Network (CNN) is developed for effective brain tumor Magnetic Resonance Imaging (MRI) classification. The developed Res-BRNet employed Regional and boundary-based operations in a systematic order within the modified spatial and residual blocks. Spatial blocks extract the brain tumor’s homogeneity and heterogeneity patterns, and boundary-related features. Additionally, the residual blocks significantly capture local and global texture variations of brain tumors. The efficiency of the developed Res-BRNet is evaluated on a standard dataset; collected from Kaggle and Figshare containing various tumor categories, including meningioma, glioma, pituitary, and healthy images. Experiments prove that the developed Res-BRNet outperforms the standard CNN models and attained excellent performances (accuracy: 98.22%, sensitivity: 0.9811, F1-score: 0.9841, and precision: 0.9822) on challenging datasets. Additionally, the performance of the proposed Res-BRNet indicates a strong potential for medical image-based disease analyses.