[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128263-en":3,"doc-seo-128263-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},128263,2336475104957,"Seraphina","https://ap-avatar.wpscdn.com/avatar/22000c4c6bd8a5076e1?x-image-process=image/resize,m_fixed,w_180,h_180&k=1787554080175789136",8,"Research & Report","Federated Vs Central Machine Learning On Diabetic Foot Ulcer Images - Comparative Simulations","Timely detection and treatment of diabetic foot ulcers (DFUs) are critical for improving patient outcomes. This thesis investigates machine learning for DFU identification using a federated learning framework to enhance both precision and privacy in medical image analysis. A U-Net segmentation model is trained collaboratively across healthcare providers while maintaining patient data confidentiality. Results show high Dice (0.9) and IoU (0.8) performance comparable to centralized training benchmarks. Albumentations-based transformations standardize images from different devices, improving generalization. Experiments also test uniform and unbalanced federated data distributions, and the work provides well-documented source code for further research.","University of North Dakota  \nUND Scholarly Commons  \n\n| Theses and Dissertations | Theses, Dissertations, and Senior Projects |\n| --- | --- |\n| January 2023\u003Cbr>Federated Vs Central Machine Learning On Diabetic Foot Ulcer Images: Comparative Simulations\u003Cbr>Mahdi Saeedi\u003Cbr>How does access to this work benefit you? Let us know!\u003Cbr>Follow this and additional works at: [https://commons.und.edu/theses](https://commons.und.edu/theses) |  |\n\nRecommended Citation  \nSaeedi, Mahdi, \"Federated Vs Central Machine Learning On Diabetic Foot Ulcer Images: Comparative Simulations\" (2023) . Theses and Dissertations. 5700.  \n[https://commons.und.edu/theses/5700](https://commons.und.edu/theses/5700)  \nThis Thesis is brought to you for free and open access by the Theses, Dissertations, and Senior Projects at UND Scholarly Commons. It has been accepted for inclusion in Theses and Dissertations by an authorized administrator of UND Scholarly Commons. For more information, please contact [und.commons@library.und.edu](und.commons@library.und.edu).  \nFEDERATED VS CENTRAL MACHINE LEARNING ON DIABETIC FOOT ULCER IMAGES:  \nCOMPARATIVE SIMULATIONS  \nBY  \nMahdi Saeedi  \nBachelor of Science, University of North Dakota, 2021  \nA Thesis  \nSubmitted to the Graduate Faculty  \nOf the  \nUniversity of North Dakota  \nIn partial fulfillment of requirements  \nFor the degree of  \nMaster of Science  \nBiomedical Engineering  \nGrand Forks, North Dakota  \nDecember 2023  \nName: Mahdi Saeedi  \nDegree: Master of Science  \nThis document, submitted in partial fulfillment of the requirements for the degree from the University of North Dakota, has been read by the Faculty Advisory Committee under whomthe work has been done and is hereby approved.  \nKouhyar Tavakolian  \n\n| Hassan Reza |\n| --- |\n| Sattar Dorafshan |\n| Hossein Kashani-Zadeh |\n\nThis document is being submitted by the appointed advisory committee as having met all the requirements of the School of Graduate Studies at the University of North Dakota and is hereby approved.  \nDean of the School of Graduate Studies  \nDate  \nPERMISSION  \nTitle FEDERATED VS CENTRAL MACHINE LEARNING ON  \nDIABETIC FOOT ULCER IMAGES: COMPARATIVE SIMULATIONS  \nDepartment Biomedical Engineering  \nDegree Master of Science  \nIn presenting this dissertation in partial fulfillment of the requirements for a graduate degree from the University of North Dakota, I agree that the library of this University shall make it freely available for inspection. I further agree that permission for extensive copying for scholarly purposes may be granted by the professor who supervised my dissertation work or, in his absence, by the Chairperson of the department or the dean of the School of Graduate Studies. It is understood that any copying or publication, or other use of this dissertation or part thereof for financial gain shall not be allowed without my written permission. It is also understood that due recognition shall be given to me and to the University of North Dakota in any scholarly use which may be made of any material in my dissertation.  \nMahdi Saeedi  \nDecember 1st, 2023  \nAbstract  \nIn the forefront of biomedical engineering, timely detection and treatment of diabetic foot ulcers (DFUs) are pivotal for enhancing patient outcomes. This study explores the advancements of machine learning, adopting a federated learning framework to augment the precision and privacy of medical image analysis for DFU identification. Federated learning, a decentralized machine learning approach, proves essential, enabling the collaborative training of convolutional neural networks (CNNs) across a network of healthcare providers while upholding the sanctity of patient data confidentiality. The study employs the U-Net architecture, acclaimed for its adeptness in biomedical image segmentation. It showcases that through federated learning, the model attains high Dice and Intersection over Union (IoU) scores, mirroring those from centralized training benchmarks, thereby maintain","cbCaij3mXy7UyOlg","https://ap.wps.com/l/cbCaij3mXy7UyOlg","pdf",3666743,1,90,"English","en",105,"# Abstract\n# Background and Motivation\n# Federated Learning Framework\n## Privacy and Decentralized Training\n## Model Architecture (U-Net)\n# Data Standardization and Augmentation\n## Albumentations\n# Experimental Setup and Data Distributions\n## Uniform vs Unbalanced Data\n# Results and Performance Metrics\n## Dice coefficient\n## Intersection over Union (IoU)\n# Code Availability and Impact","[{\"question\":\"How does the thesis use federated learning for diabetic foot ulcer image analysis?\",\"answer\":\"It trains a U-Net model collaboratively across multiple healthcare providers using a federated learning framework, keeping patient data confidential while improving DFU identification.\"},{\"question\":\"What performance metrics are reported for the federated model?\",\"answer\":\"The federated U-Net achieves a Dice coefficient of 0.9 and an Intersection over Union (IoU) score of 0.8, matching centralized training benchmarks in many cases and sometimes surpassing them.\"},{\"question\":\"Why are Albumentations and data transformations important in this work?\",\"answer\":\"They standardize images across different imaging devices, addressing variability from camera specifications and improving the model’s ability to generalize across data sources.\"}]","Federated Vs Central Machine Learning On Diabetic Foot Ulcer Images - Comparative Simulations | PDF",1785946320,227,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"federated-vs-central-machine-learning-on-diabetic-foot-ulcer-images-comparative-simulations","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/federated-vs-central-machine-learning-on-diabetic-foot-ulcer-images-comparative-simulations/128263/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"How does the thesis use federated learning for diabetic foot ulcer image analysis?","Question",{"text":75,"@type":76},"It trains a U-Net model collaboratively across multiple healthcare providers using a federated learning framework, keeping patient data confidential while improving DFU identification.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What performance metrics are reported for the federated model?",{"text":80,"@type":76},"The federated U-Net achieves a Dice coefficient of 0.9 and an Intersection over Union (IoU) score of 0.8, matching centralized training benchmarks in many cases and sometimes surpassing them.",{"name":82,"@type":73,"acceptedAnswer":83},"Why are Albumentations and data transformations important in this work?",{"text":84,"@type":76},"They standardize images across different imaging devices, addressing variability from camera specifications and improving the model’s ability to generalize across data sources.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,96,100,104,109,114,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":21,"slug":95},"Story & Novel","story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":97,"show_sort_weight":98,"slug":99},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":101,"show_sort_weight":102,"slug":103},"Exam",70,"exam",{"id":105,"doc_module":4,"doc_module_name":46,"category_name":106,"show_sort_weight":107,"slug":108},5,"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":105,"slug":137},19,"General","general"]