Improving Classification of Medical Images Using ESRGAN-Based Upscaling and MobileNetV2

Low-resolution photos are frequently problematic in the medical field when diagnosing skin and eye conditions since they can induce noise and lower the precision of classification algorithms. To overcome this, this research implements the Enhanced Super-Resolution Generative Adversarial Network (ESRGAN) method which is used to perform upscaling, namely increasing the resolution of a low image to a high-resolution image. The research results show that ESRGAN is able to improve the quality of eye and skin images, as proven by accuracy consistency tests on the two datasets. For image classification, the MobileNetV2 model is used because this model is suitable for eye and skin datasets. Evaluation of the image retrieval system using a high-resolution dataset resulting from ESRGAN Upscaling shows an increase in accuracy of 4-17% on both datasets. In this research, the improvement in visual image quality is also proven by the high Peak Signal-to-Noise Ratio (PSNR) value, so that ESRGAN is proven to be effective in increasing image resolution and clarity, both for eye medical image datasets and skin images.