TumorGANet: A Transfer Learning and Generative Adversarial Network- Based Data Augmentation Model for Brain Tumor Classification

Abstract

Diagnosing brain tumors using magnetic resonance imaging (MRI) presents significant challenges due to the complexities of segmentation and the variability in tumor characteristics. To address the limitations inherent in traditional methods, this research employs an advanced deep learning approach, integrating ResNet50 for feature extraction and Generative Adversarial Networks (GANs) for data augmentation. A comprehensive evaluation of ten transfer learning algorithms, including GoogLeNet and VGG-16, was conducted for the classification of brain tumors. Model performance was assessed using precision, recall, and F1-score metrics, complemented by additional metrics such as Hamming loss and the Matthews correlation coefficient to provide a more comprehensive insight. To ensure transparency in image predictions, Explainable AI techniques, specifically Local Interpretable Model-Agnostic Explanations (LIME), were utilized. The study involved the analysis of 7023 MRI images, with TumorGANet being trained on a dataset encompassing gliomas, meningiomas, non-tumorous cases, and pituitary tumors. The results demonstrate the exceptional performance of proposed model named TumorGANet, achieving an accuracy of 99.53%, precision and recall rates of 100%, F1 scores of 99%, and a Hamming loss of 0.2%.

Keywords

• Brain imaging
• brain tumor
• transfer learning
• generative adversarial network
• explainable AI