Evaluating Convolutional Neural Networks for Class Imbalance in Mars Image Classification

Deep learning (DL), particularly the use of Convolutional Neural Networks (CNNs), has played a significant role in computer vision, including image recognition and classification. This study investigates the performance of three foundational CNN architectures — AlexNet, ResNet, and VGGNet – in addressing class imbalances within image datasets. These architectures are chosen due to their historical significance and widespread adoption as benchmarks, allowing for a consistent evaluation of performance across different scenarios. Specifically, we assess their efficacy using a NASA Mars image dataset characterized by varying levels of class imbalance. Our analysis focuses on comparing how each architecture performs under different imbalance scenarios, ranging from mild to severe. The empirical results demonstrate that while all three models effectively manage mild imbalances, AlexNet’s performance significantly declines as imbalance severity increases, unlike ResNet and VGGNet, which exhibit considerable robustness even under highly imbalanced conditions.