[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119219-en":3,"doc-seo-119219-105":30,"detail-sidebar-cat-0-en-105":92},{"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":20,"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},119219,1099514068035,"Ezra","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",7,"Healthcare","Multiple sclerosis diagnosis with deep learning and explainable AI","Diagnosing multiple sclerosis (MS) is difficult because imaging interpretation is complex and often relies on subjective clinical judgment. Deep learning models can improve performance but may hinder adoption due to opaque decision-making. This study integrates eXplainable Artificial Intelligence (XAI) into a CNN diagnosis pipeline by combining EfficientNetB1 with Grad-CAM to visualize model attention. Training uses FLAIR axial and sagittal MRI images from MS patients and healthy individuals, then applies bias/irrelevant-feature checks and 10-fold cross-validation, achieving very high accuracy and demonstrating generalization on an independent dataset for improved clinical reliability.","Technological University Dublin  \nARROW@TU Dublin  \n\n| Conference papers | School of Electrical and Electronic Engineering |\n| --- | --- |\n| 2024\u003Cbr>Multiple sclerosis diagnosis with deep learning and explainable AI\u003Cbr>Nighat Bibi\u003Cbr>Technological University Dublin, [d22125041@mytudublin.ie](d22125041@mytudublin.ie)\u003Cbr>Jane Courtney\u003Cbr>Technological University Dublin, Ireland\u003Cbr>Kathleen M. Curran\u003Cbr>University College Dublin, Ireland\u003Cbr>Follow this and additional works at: [https://arrow.tudublin.ie/engscheleart](https://arrow.tudublin.ie/engscheleart)\u003Cbr> Part of the Engineering Commons |  |\n\nRecommended Citation  \nBibi, Nighat; Courtney, Jane; and Curran, Kathleen M., \"Multiple sclerosis diagnosis with deep learning and explainable AI\" (2024) . Conference papers. 393.  \n[https://arrow.tudublin.ie/engscheleart/393](https://arrow.tudublin.ie/engscheleart/393)  \nThis Conference Paper is brought to you for free and open access by the School of Electrical and Electronic Engineering at ARROW@TU Dublin. It has been accepted for inclusion in Conference papers by an authorized administrator of ARROW@TU Dublin. For more information, please contact [arrow.admin@tudublin.ie](arrow.admin@tudublin.ie), [aisling.coyne@tudublin.ie](aisling.coyne@tudublin.ie), [vera.ki](vera.ki)[lshaw@tudublin.ie](lshaw@tudublin.ie).  \nThis work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 International License. Funder: Science Foundation Ireland Center for Research Training in Machine Learning  \nMedical Image Understanding and Analysis  \nManchester, UK  \nMedical Image Understanding and Analysis  \nMultiple sclerosis diagnosis with deep learning and explainable AI  \nAuthor  \nNighat Bibi – School of Electrical and Electronic Engineering, Technological University Dublin, Dublin, Ireland  \nJane Courtney – School of Electrical and Electronic Engineering, Technological University Dublin, Dublin, Ireland  \nKathleen M. Curran – University College Dublin, School of Medicine, UCD Belfield, Dublin 4, Ireland  \nCitation  \nBibi, N., Courtney, J., Curran, M. K. Multiple sclerosis diagnosis with deep learning and explainable AI.  \nAbstract  \nDiagnosing multiple sclerosis (MS) presents significant challenges due to its complex clinical presentation and the subjective interpretation of imaging findings. Machine learning (ML) and deep learning (DL) models, despite their potential, often exacerbate these challenges with their opaque decision-making processes, hindering clinical integration. This study addresses these limitations by employing eXplainable Artificial Intelligence (XAI) techniques, specifically integrating Grad-CAM within a Convolutional Neural Network (CNN) framework, EfficientNetB1, for the diagnosis of MS. The primary objective is to enhance the transparency and reliability of MS diagnosis by providing clear visual insights into the model’s decision-making process, while also identifying and mitigating potential biases and irrelevant features. Using a dataset comprising FLAIR axial and sagittal MRI images of MS patients and healthy individuals, the CNN model is trained and integrated with Grad-CAM. Post-integration observations revealed  \n69 [frontiersin.org](frontiersin.org)  \nMedical Image Understanding and Analysis  \npotential biases and irrelevant features, particularly in the erroneous highlighting of certain regions by the model. Subsequent adjustments and re-training using 10-fold cross-validation led to an improved model with accuracy rates of 99.82% for axial, 99.76% for sagittal images, and 99.36% overall. Furthermore, testing on a separate dataset confirmed the model’s ability to generalize and perform well across various clinical contexts. In conclusion, this study underscores the critical role of transparent and interpretable models in medical diagnostics, demonstrating that the integration of XAI techniques can significantly enhance the reliability and clinical applicability of models.  \nDataset  \nThe dataset use","cbCaiamfSRHB3ABs","https://ap.wps.com/l/cbCaiamfSRHB3ABs","pdf",356824,1,9,"English","en",105,"# Abstract\n## Dataset\n## Methods & Results","[{\"question\":\"Why is multiple sclerosis (MS) diagnosis challenging for clinicians and models?\",\"answer\":\"MS imaging findings are complex and their interpretation is often subjective. Opaque model decisions from deep learning can further hinder clinical integration.\"},{\"question\":\"How does the study make the deep learning model explainable?\",\"answer\":\"It integrates Grad-CAM into an EfficientNetB1 convolutional neural network framework to generate visual insights into where the model focuses for predictions.\"},{\"question\":\"What results and evaluation approach does the study report?\",\"answer\":\"The model is trained on FLAIR axial and sagittal MRI images and evaluated using 10-fold cross-validation, reaching accuracy up to about 99.82% for axial, and is further tested on a separate dataset to confirm generalization across clinical contexts.\"}]","Multiple sclerosis diagnosis with deep learning and explainable AI | PDF",1785723153,23,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"multiple-sclerosis-diagnosis-with-deep-learning-and-explainable-ai","",{"@graph":36,"@context":86},[37,54,69],{"@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/healthcare/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/multiple-sclerosis-diagnosis-with-deep-learning-and-explainable-ai/119219/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05","2026-08-03",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Why is multiple sclerosis (MS) diagnosis challenging for clinicians and models?","Question",{"text":76,"@type":77},"MS imaging findings are complex and their interpretation is often subjective. Opaque model decisions from deep learning can further hinder clinical integration.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does the study make the deep learning model explainable?",{"text":81,"@type":77},"It integrates Grad-CAM into an EfficientNetB1 convolutional neural network framework to generate visual insights into where the model focuses for predictions.",{"name":83,"@type":74,"acceptedAnswer":84},"What results and evaluation approach does the study report?",{"text":85,"@type":77},"The model is trained on FLAIR axial and sagittal MRI images and evaluated using 10-fold cross-validation, reaching accuracy up to about 99.82% for axial, and is further tested on a separate dataset to confirm generalization across clinical contexts.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,119,124,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":117,"slug":118},40,"healthcare",{"id":120,"doc_module":4,"doc_module_name":46,"category_name":121,"show_sort_weight":122,"slug":123},8,"Research & Report",30,"research-report",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":107,"slug":138},19,"General","general"]