BrainCNN: Automated Brain Tumor Grading from Magnetic Resonance Images Using a Convolutional Neural Network-Based Customized Model.
Authors
Affiliations (9)
Affiliations (9)
- Center of Research for Cyber Security and Network (CSNET), Faculty of Computer Science and Information Technology, Universiti Malaya, 50603 Kuala Lumpur, Malaysia.
- Biophotonics Imaging Techniques Laboratory, Institute of Physics, The Islamia University of Bahawalpur, Pakistan. Electronic address: [email protected].
- Biophotonics Imaging Techniques Laboratory, Institute of Physics, The Islamia University of Bahawalpur, Pakistan. Electronic address: [email protected].
- Department of Artificial Intelligence, Faculty of Computing, The Islamia University of Bahawalpur, Pakistan. Electronic address: [email protected].
- Center of Research for Cyber Security and Network (CSNET), Faculty of Computer Science and Information Technology, Universiti Malaya, 50603 Kuala Lumpur, Malaysia. Electronic address: [email protected].
- Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia. Electronic address: [email protected].
- Automated Systems and Soft Computing Lab (ASSCL), Computer Science Department, Prince Sultan University, Riyadh 11586, Saudi Arabia; Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf 32952, Egypt. Electronic address: [email protected].
- Computer Science Department, College of Science and Humanities, Imam Abdulrahman Bin Faisal University, Jubail 31961, Saudi Arabia. Electronic address: [email protected].
- College of Mathematics and Computer Science, Zhejiang A&F University, Hangzhou 311300, China; Division of Research and Development, Lovely Professional University, Phagwara, India; Center of Research Impact and Outcome, Chitkara University, Rajpura, 140401, Punjab, India. Electronic address: [email protected].
Abstract
Brain tumors pose a significant risk to human life, making accurate grading essential for effective treatment planning and improved survival rates. Magnetic Resonance Imaging (MRI) plays a crucial role in this process. The objective of this study was to develop an automated brain tumor grading system utilizing deep learning techniques. A dataset comprising 293 MRI scans from patients was obtained from the Department of Radiology at Bahawal Victoria Hospital in Bahawalpur, Pakistan. The proposed approach integrates a specialized Convolutional Neural Network (CNN) with pre-trained models to classify brain tumors into low-grade (LGT) and high-grade (HGT) categories with high accuracy. To assess the model's robustness, experiments were conducted using various methods: (1) raw MRI slices, (2) MRI segments containing only the tumor area, (3) feature-extracted slices derived from the original images through the proposed CNN architecture, and (4) feature-extracted slices from tumor area-only segmented images using the proposed CNN. The MRI slices and the features extracted from them were labeled using machine learning models, including Support Vector Machine (SVM) and CNN architectures based on transfer learning, such as MobileNet, Inception V3, and ResNet-50. Additionally, a custom model was specifically developed for this research. The proposed model achieved an impressive peak accuracy of 99.45%, with classification accuracies of 99.56% for low-grade tumors and 99.49% for high-grade tumors, surpassing traditional methods. These results not only enhance the accuracy of brain tumor grading but also improve computational efficiency by reducing processing time and the number of iterations required.