Performance Comparison of GAN-Augmented and Traditional CNN Models for Spinal Cord Tumor Detection

Detecting spinal cord tumors using MRI scans is a very challenging issue within clinical radiology. The availability of high-quality, expertly annotated datasets which can be useful for spinal cord tumor detection are limited since the intricate anatomy of the spinal cord complicates image interpretation and this tumor is comparatively rare. Convolutional Neural Network (CNNs) have emerged as reliable solution for automating this detection process; however, they often tend to fail when they are provided with limited or imbalanced datasets. This limitation can be overcome using Generative Adversarial Networks (GANs) by generating realistic synthetic MRI images in order to enhance dataset diversity and effectively mitigate class imbalance. In this paper, we have provided one-qualitative comparison of GAN-augmented CNNs versus traditional CNNs for spinal cord tumor detection.  We have tried to demonstrate how GAN-based augmentation improves model accuracy, generalization, robustness and overall classification metrics. We have also discussed current limitations, particularly the difficulty of training stable GANs and the underutilization of full 3D volumetric data. Moving forward, we have mentioned challenges and key directions for future work.