Transfer learning for accurate brain tumor classification in MRI: a step forward in medical diagnostics.
Authors
Affiliations (9)
Affiliations (9)
- Department of Software, Faculty of Artificial Intelligence and Software, Gachon University, Seongnam-si, Gyeonggido, 13120, Republic of Korea.
- Faculty of Information Technology and Computer Science, University of Central Punjab, Lahore, 54000, Pakistan.
- Department of Computer Science, Bahria University, Lahore Campus, Lahore, 54000, Pakistan.
- Department of Forensic Sciences, University of Health Sciences, Lahore, 54000, Pakistan.
- University College, Korea University, Seoul, 02841, Republic of Korea.
- School of Computer Science, National College of Business Administration and Economics, Lahore, 54000, Pakistan. [email protected].
- Department of Computer Science, School Education Department, Government of Punjab, Layyah, 31200, Pakistan. [email protected].
- Department of Computer Engineering, COMSATS University Islamabad, Sahiwal Campus, Sahiwal, 57000, Pakistan.
- Department of Communications and Electronics Engineering, Faculty of Engineering, University of Modern Sciences (UMS), 00967, Sana, Yemen. [email protected].
Abstract
Brain tumor classification is critical for therapeutic applications that benefit from computer-aided diagnostics. Misdiagnosing a brain tumor can significantly reduce a patient's chances of survival, as it may lead to ineffective treatments. This study proposes a novel approach for classifying brain tumors in MRI images using Transfer Learning (TL) with state-of-the-art deep learning models: AlexNet, MobileNetV2, and GoogleNet. Unlike previous studies that often focus on a single model, our work comprehensively compares these architectures, fine-tuned specifically for brain tumor classification. We utilize a publicly available dataset of 4,517 MRI scans, consisting of three prevalent types of brain tumors-glioma (1,129 images), meningioma (1,134 images), and pituitary tumors (1,138 images)-as well as 1,116 images of normal brains (no tumor). Our approach addresses key research gaps, including class imbalance, through data augmentation and model efficiency, leveraging lightweight architectures like MobileNetV2. The GoogleNet model achieves the highest classification accuracy of 99.2%, outperforming previous studies using the same dataset. This demonstrates the potential of our approach to assist physicians in making rapid and precise decisions, thereby improving patient outcomes. The results highlight the effectiveness of TL in medical diagnostics and its potential for real-world clinical deployment. This study advances the field of brain tumor classification and provides a robust framework for future research in medical image analysis.