| An Enhanced Deep Super-Resolution Generative Adversarial Network Approach for Skin Disease Image Regeneration and Assessment |
| Paper ID : 1087-ICEEM2023 (R1) |
| Authors: |
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Walid El-Shafai *1, Ibrahim Abd El-Fattah2, Taha E.Taha3 1Electronics and Electrical Communications Engineering, Faculty of Electronics Engineering, Menoufia University, Menouf, Menoufia, Egypt 2Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, 32952, Egypt 3Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, 32952, Egypt. |
| Abstract: |
| Skin cancer is a critical medical concern, posing significant challenges in accurate diagnosis. Algorithmic approaches have seen remarkable advancements across various occupations, including skin disease assessment. This research introduces a novel Enhanced Deep Super-Resolution Generative Adversarial Network (E_DSUR-GAN) methodology for regenerating low-resolution (LOR) skin disease images into super-resolution (SUR) format. Additionally, a modified SUR-dataset inspired by HAM10000 is presented for skin disease applications. The proposed approach incorporates a novel loss function design to provide supplementary information, facilitating the creation of high-quality SUR images. Experimental results demonstrate that our method outperforms existing approaches on the HAM10000 dataset. A comprehensive evaluation, employing metrics such as Peak Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE), Structural Similarity Index (S-SIM), and Multiscale Structural Similarity Index (MS-SSIM), is conducted to assess the effectiveness, training periods, and memory requirements of the proposed framework. The outcomes reveal that the suggested model excels in restoring and identifying hue and texture compared to conventional and earlier models. |
| Keywords: |
| GAN, Deep learning, Skin images, SR-GAN |
| Status : Paper Accepted (Oral Presentation) |
