{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/a-unified-technique-for-entropy-enhancement","title":"A Unified Technique for Entropy Enhancement Based Diabetic Retinopathy Detection Using Hybrid Neural Network","arxiv_id":null,"date":"2020-07-01","proceeding":"journal 2020 7","authors":["Fatima","M. Imran","A. Ullah","M. Arif","R. Noor"],"abstract":"In this paper, a unified technique for entropy enhancement-based diabetic retinopathy detection using a hybrid neural network is proposed for diagnosing diabetic retinopathy. Medical images play crucial roles in the diagnosis, but two images representing two different stages of a disease look alike. It, consequently, make the process of diagnosis extraneous and error-prone. Therefore, in this paper, a technique is proposed to address these issues. Firstly, a novel entropy enhancement technique is devised exploiting the discrete wavelet transforms to improve the visibility of the medical images by making the subtle features more prominent. Later, we designed a computationally efficient hybrid neural network that efficiently classifies diabetic retinopathy images. To examine the effectiveness of our technique, we have chosen three datasets: Ultra-Wide Filed (UWF) dataset, Asia Pacific Tele Ophthalmology Society (APTOS) dataset, and MESSIDOR-2 dataset. In the end, we performed extensive experiments to validate the performance of our technique. In addition, the comparison of the proposed scheme – in terms of accuracy, specificity, sensitivity, precision and recall curve, and area under the curve – with some of the best contemporary schemes shows the significant improvement of our techniques in terms of diabetic retinopathy classification.","url_abs":"https://www.sciencedirect.com/science/article/pii/S0010482522002165?dgcid=author","url_pdf":"https://www.sciencedirect.com/science/article/pii/S0010482522002165?dgcid=author","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"a-unified-technique-for-entropy-enhancement","repo_url":"https://github.com/ImranNust/DiabeticRetinoPathyDetection","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"diabetic-retinopathy-detection","task_name":"Diabetic Retinopathy Detection"},{"task_slug":"specificity","task_name":"Specificity"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}