Enhancing medical image classification based on super-resolution techniques: A comparative study

This paper explores the application of super-resolution techniques in the context of medical image classification, focusing on the impact of various super-resolution models on classification model performance. With the burgeoning influence of deep learning in the field of medical image analysis. This research discusses the potential of image super-resolution to enhance the precision of medical image categorization. This paper compares three super-resolution methods: bicubic interpolation, Super Resolution Residual network (SRResnet), and Super Resolution Generative Adversarial Networks (SRGAN), using the OCT2017 dataset for optical coherence tomography (OCT) images. The experiments reveal that deep learning-based super-resolution methods surpass bicubic interpolation in boosting classification model accuracy. Notably, SRResnet stands out as the preferred choice for enhancing classification accuracy, despite yielding less visually appealing results compared to SRGAN. This suggests that while Generative Adversarial Networks (GANs) and perceptual loss functions hold promise in super-resolution, their translation to improved classification model performance may vary. This papers findings provide guidance for practitioners and researchers in the field, emphasizing that selecting an appropriate super-resolution method could optimize classification models for medical image analysis.