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

• URL: http://arxiv.org/abs/2211.16571v1
• Date: Tue, 29 Nov 2022 20:14:13 GMT
• Title: Brain Tumor MRI Classification using a Novel Deep Residual and Regional CNN
• Authors: Mirza Mumtaz Zahoor, Saddam Hussain Khan
• Abstract summary: 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 efficiency of the developed Res-BRNet is evaluated on a standard dataset; collected from Kaggle and Figshare containing various tumor categories. Experiments prove that the developed Res-BRNet outperforms the standard CNN models and attained excellent performances.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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