An Integrated Design for Segmentation and Classification of Diabetic Foot Ulcers using Thermography Images

An Integrated Design for Segmentation and Classification of Diabetic Foot Ulcers using Thermography Images | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article An Integrated Design for Segmentation and Classification of Diabetic Foot Ulcers using Thermography Images YAMUNARANI THANIKACHALAM, Wan Suhaimizan Bin Wan Zaki, Ashok Vajravelu, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7593850/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 03 Jan, 2026 Read the published version in Journal of Diabetes & Metabolic Disorders → Version 1 posted You are reading this latest preprint version Abstract In recent days, diabetics, a chronic disease has risen significantly which leads to more health complications. Among those complications, diabetic foot ulcer (DFU) is much serious. DFU is a wound on the foot of a person who is affected with diabetics. It sometimes leads to fatality if untreated. Diagnosing the DFU in its early stage remains challenging due to medical impediments by the diabetics. Thermography serves as a promising technique in the early prediction of the DFU and aids for an improvised treatment towards the eradication of foot amputations. But still, utilizing thermography images for clinical treatments continues to be underexplored in treating DFU due to its computational complexities and existence of ambiguities in thermal images. To overcome this challenge, this research paper proposes an Intelligent Prediction System (IPS) using the modified swin transformers for an effective segmentation and deep capsule networks for an accurate prediction of DFU. In the segmentation phase, swin transformers can be used as U-NET based architecture to segment the lesions of foot ulcers. Deep features are extracted by the capsule networks and supplied to the deep shallow network which works on the standard of extreme learning networks to achieve the early prediction of DFU. The extensive experimentation is conducted using the thermal foot ulcer images in Python3.20 and Tensorflow –Keras Libraries. To verify the efficiency of the proposed schema, evaluated performances are assessed with other research experiments. Results show that the proposed schema achieves the highest prediction accuracy (99%) with promising segmented performance (98.6%). Moreover, the proposed model excels the varied residing schema and establishes a firm foothold in the early prediction of DFUs. Diabetic Foot Ulcers Capsule Networks Swin Transformers Shallow Network Extreme Learning principles Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 03 Jan, 2026 Read the published version in Journal of Diabetes & Metabolic Disorders → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7593850","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":523557447,"identity":"7d3c8a08-0bbc-41f5-ac3f-ea0255bc38f1","order_by":0,"name":"YAMUNARANI 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Among those complications, diabetic foot ulcer (DFU) is much serious. DFU is a wound on the foot of a person who is affected with diabetics. It sometimes leads to fatality if untreated. Diagnosing the DFU in its early stage remains challenging due to medical impediments by the diabetics. Thermography serves as a promising technique in the early prediction of the DFU and aids for an improvised treatment towards the eradication of foot amputations. But still, utilizing thermography images for clinical treatments continues to be underexplored in treating DFU due to its computational complexities and existence of ambiguities in thermal images. To overcome this challenge, this research paper proposes an Intelligent Prediction System (IPS) using the modified swin transformers for an effective segmentation and deep capsule networks for an accurate prediction of DFU. In the segmentation phase, swin transformers can be used as U-NET based architecture to segment the lesions of foot ulcers. 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