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

TL;DR

This paper introduces Res-BRNet, a novel deep residual and regional CNN designed for accurate brain tumor MRI classification, outperforming standard models with high accuracy and robustness.

Contribution

The paper presents a new deep residual and regional CNN architecture, Res-BRNet, incorporating boundary-based operations for improved brain tumor classification from MRI images.

Findings

Res-BRNet achieved 98.22% accuracy on standard datasets.

The model outperformed traditional CNNs in sensitivity and F-score.

Res-BRNet shows strong potential for medical image analysis.

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. Moreover, the spatial block extract homogeneity and boundary-defined features at the abstract level. Furthermore, the residual blocks employed at the target level significantly learn local and global texture variations of different classes 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, F-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.