Combined Oriented Data Augmentation Method for Brain MRI Images

Farhan, Ahmeed Suliman; Khalid, Muhammad; Manzoor, Umar

Abstract

In recent years, deep learning’s use in medical imaging has grown exponentially. However, one of the biggest problems with training deep learning models is the unavailability of large amounts of data, which leads to overfitting. Collecting large quantities of labelled medical images is expensive, time-consuming, and depends on specialists’ availability. In this paper, we proposed a novel method namely Oriented Combination MRI (OCMRI) for augmenting brain MRI dataset. The proposed method helps CNN models overcome overfitting and class imbalance problems by combining Brain MRI images to generate new images. The image fusion is performed by selecting two images of the same tumor class if the Mean Squared Error (MSE) between these two images is greater than threshold 1 and lower than threshold 2. Both thresholds are variable, initially set by the user and automatically fine-tuned by the algorithm to control the number of images produced for each class, thus helping to address the data imbalance problem. The proposed approach was evaluated by training and testing the PRCnet model on four publicly available datasets before and after applying the proposed method to the datasets. Where the classification accuracy without data augmentation was 85.19% for dataset A, 90.12% for dataset B, 94.77% for dataset C, and 90% for dataset D respectively. After adding the synthetic data; the accuracy improved to 92.7% for dataset A, 95.37% for dataset B, 96.51% for dataset C and 98% for dataset D respectively.

Citation

Farhan, A. S., Khalid, M., & Manzoor, U. (2025). Combined Oriented Data Augmentation Method for Brain MRI Images. IEEE Access, 13, 9981-9994. https://doi.org/10.1109/ACCESS.2025.3526684

• Journal Article Type: Article
• Acceptance Date: Dec 31, 2024
• Online Publication Date: Jan 7, 2025
• Publication Date: Jan 1, 2025
• Deposit Date: Feb 10, 2025
• Publicly Available Date: Feb 10, 2025
• Journal: IEEE Access
• Electronic ISSN: 2169-3536
• Publisher: Institute of Electrical and Electronics Engineers (IEEE)
• Peer Reviewed: Peer Reviewed
• Volume: 13
• Pages: 9981-9994
• DOI: https://doi.org/10.1109/ACCESS.2025.3526684
• Public URL: https://hull-repository.worktribe.com/output/5006008