Elevating Diagnostic Accuracy: Advanced GAN-Enhanced High-Resolution Medical Imaging for Superior Disease Detection
Advanced imaging in medical has become crucial in the early identify diseases because they reveal the important structural features of the human body. But it is almost impossible to get such high resolution images in real life situation due to the factors such as image capture and processing equipment, and environmental factors that affect the outcome of the image. This work proposes a sub-type of GAN that is used in enhancement of images particularly in medical fields. The generator of the Med-GAN extracts a high-resolution image from a low-resolution one with the help of novel features learned by the model. The approach of reconstructing high resolution from multiple parallel streams of lower resolution employs deconvolution algorithms with multiple scale fusions that produce better high resolution representations as compared to the technique of bilinear interpolation. The performances of the proposed Med-GAN are tested on two publicly available COVID-19 CT datasets and one private medical image dataset which shows that the proposed method outperforms the existing methods in performance comparisons. Consequently, for PSNR, the score improves from 24.103 dB corresponding to the Initial Approach of the “BRaTS (FLAIR)” dataset to 25.496 dB for the Proposed Method; whereas for SSIM the score increases from 0.782 to 0.812.se types of high-resolution images are usually impossible to get due to limits in imaging devices, environmental conditions, and human factors. This work proposes the Med-GAN: an Enhanced Super-Resolution Generative Adversarial Network tuned for medical image enhancement. The Med-GAN generator learns high-resolution representations from low-resolution images via advanced feature extraction methods. Deconvolution algorithms with multi-scale fusions recover better high-resolution representations from multiple parallel streams of lower resolutions in this approach compared to traditional bilinear interpolation methods. Evaluated on two publicly available COVID-19 CT datasets and one custom medical image dataset, the proposed Med-GAN significantly outperforms existing techniques in performance comparisons. In particular, PSNR rises from 24.103 dB for the "BRaTS (FLAIR)" dataset in the Initial Approach to 25.496 dB in the Proposed Method, while SSIM increases from 0.782 to 0.812. If that is the case then it could be said that the solution of the proposed Med-GAN is one of the most realistic means for improving the quality of medical images and therefore contributes to better diagnostics of diseases
Volume & Issue
Vol. Volume 17 / Iss. Issue 2