Enhancing Histological Cancer Cell Detection: Integrating XAI with Deep Learning for Improved Accuracy, Interpretability, and Clinical Trust

Enhancing Histological Cancer Cell Detection: Integrating XAI with Deep Learning for Improved Accuracy, Interpretability, and Clinical Trust | 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 Article Enhancing Histological Cancer Cell Detection: Integrating XAI with Deep Learning for Improved Accuracy, Interpretability, and Clinical Trust Apurba Roy, Shamaeta Paul Gupta, Muhaiminul Islam, Shifat E. Arman, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8157817/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 21 You are reading this latest preprint version Abstract A precise and early histological diagnosis is critical for effective cancer treatment, reducing cancer-associated disability, and improving long-term quality of life. However, traditional deep learning (DL) models often function as black boxes, which limit their clinical adoption due to a lack of interpretability. To address this challenge, we propose a novel framework that integrates deep learning with Explainable Artificial Intelligence (XAI) to enhance both diagnostic accuracy and interpretability in histological cancer cell detection. The framework utilizes an ensemble of three pre-trained convolutional neural networks (CNNs): ResNet50, VGG16, and InceptionV3, combined with a 4x UltraSharp super-resolution technique to improve image clarity. XAI methods: Grad-CAM, SHAP, and LIME are incorporated to provide transparency into the model’s decision-making process. Our quantitative results show substantial improvements (1%-10%) in classification accuracy following upscaling, with ResNet50 achieving 96.21%, VGG16 92.14%, and InceptionV3 96.42%. This integrated approach addresses the challenge of achieving high accuracy on heterogeneous datasets where tissue sources are unspecified. Strong correlations were observed between the tumor regions identified by the models and the heatmaps generated by the XAI techniques. The framework’s interpretability and diagnostic performance were clinically validated by expert pathologists and further supported by a human trust survey involving 38 participants. This study demonstrates that integrating deep learning with XAI not only improves diagnostic performance but also fosters trust in AI-assisted digital pathology, paving the way for clinically reliable and interpretable AI systems in cancer detection. Biological sciences/Cancer Biological sciences/Computational biology and bioinformatics Physical sciences/Mathematics and computing Health sciences/Medical research Health sciences/Oncology Super-Resolution Histological Diagnosis Disability Research Deep Learning Explainable AI CNN Tumor Detection Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 06 Jan, 2026 Reviewers agreed at journal 06 Jan, 2026 Reviews received at journal 06 Jan, 2026 Reviewers agreed at journal 06 Jan, 2026 Reviews received at journal 06 Jan, 2026 Reviewers agreed at journal 06 Jan, 2026 Reviewers agreed at journal 03 Jan, 2026 Reviewers agreed at journal 03 Jan, 2026 Reviews received at journal 29 Dec, 2025 Reviewers agreed at journal 28 Dec, 2025 Reviews received at journal 24 Dec, 2025 Reviewers agreed at journal 14 Dec, 2025 Reviewers agreed at journal 13 Dec, 2025 Reviewers agreed at journal 08 Dec, 2025 Reviews received at journal 06 Dec, 2025 Reviewers agreed at journal 03 Dec, 2025 Reviewers invited by journal 01 Dec, 2025 Editor invited by journal 26 Nov, 2025 Editor assigned by journal 20 Nov, 2025 Submission checks completed at journal 20 Nov, 2025 First submitted to journal 19 Nov, 2025 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-8157817","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":548148357,"identity":"a6b569af-2f7b-483c-96a0-7a2756ae679c","order_by":0,"name":"Apurba Roy","email":"","orcid":"","institution":"University of Dhaka","correspondingAuthor":false,"prefix":"","firstName":"Apurba","middleName":"","lastName":"Roy","suffix":""},{"id":548148358,"identity":"171ebf80-f8eb-454f-8bc5-ffc237c2519d","order_by":1,"name":"Shamaeta Paul Gupta","email":"","orcid":"","institution":"University of Dhaka","correspondingAuthor":false,"prefix":"","firstName":"Shamaeta","middleName":"Paul","lastName":"Gupta","suffix":""},{"id":548148359,"identity":"cdcf297e-651b-4038-9ab8-ba31e68ab111","order_by":2,"name":"Muhaiminul Islam","email":"","orcid":"","institution":"University of Dhaka","correspondingAuthor":false,"prefix":"","firstName":"Muhaiminul","middleName":"","lastName":"Islam","suffix":""},{"id":548148360,"identity":"23486f5d-91b7-4a2b-aa35-63d99de13385","order_by":3,"name":"Shifat E. 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However, traditional deep learning (DL) models often function as black boxes, which limit their clinical adoption due to a lack of interpretability. To address this challenge, we propose a novel framework that integrates deep learning with Explainable Artificial Intelligence (XAI) to enhance both diagnostic accuracy and interpretability in histological cancer cell detection. The framework utilizes an ensemble of three pre-trained convolutional neural networks (CNNs): ResNet50, VGG16, and InceptionV3, combined with a 4x UltraSharp super-resolution technique to improve image clarity. XAI methods: Grad-CAM, SHAP, and LIME are incorporated to provide transparency into the model’s decision-making process. Our quantitative results show substantial improvements (1%-10%) in classification accuracy following upscaling, with ResNet50 achieving 96.21%, VGG16 92.14%, and InceptionV3 96.42%. This integrated approach addresses the challenge of achieving high accuracy on heterogeneous datasets where tissue sources are unspecified. Strong correlations were observed between the tumor regions identified by the models and the heatmaps generated by the XAI techniques. The framework’s interpretability and diagnostic performance were clinically validated by expert pathologists and further supported by a human trust survey involving 38 participants. This study demonstrates that integrating deep learning with XAI not only improves diagnostic performance but also fosters trust in AI-assisted digital pathology, paving the way for clinically reliable and interpretable AI systems in cancer detection.","manuscriptTitle":"Enhancing Histological Cancer Cell Detection: Integrating XAI with Deep Learning for Improved Accuracy, Interpretability, and Clinical Trust","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-21 02:37:40","doi":"10.21203/rs.3.rs-8157817/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-06T10:05:47+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"329426333461409725385442603593276578865","date":"2026-01-06T09:38:44+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-06T06:46:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"62535358829672665380938445375334708651","date":"2026-01-06T06:39:52+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-06T06:05:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"16518419852164011559567744338875970885","date":"2026-01-06T05:19:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"301115079426834626158797614361389800768","date":"2026-01-03T15:17:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"96074318553605540746004404045463585324","date":"2026-01-03T13:28:53+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-29T09:39:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"36293161505319689661237278809023911698","date":"2025-12-28T11:09:43+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-24T12:36:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"191229242158865686533102607511231432920","date":"2025-12-14T05:52:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"249929089640242282302651944493214948480","date":"2025-12-13T19:33:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"145131015113931756522689816184765553712","date":"2025-12-08T10:01:00+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-06T12:35:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"112828405447608084755993279403915218793","date":"2025-12-03T07:14:47+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-01T14:12:22+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-11-26T12:02:54+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-20T12:42:25+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-20T12:40:25+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-11-19T17:38:14+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3d4c4e80-b70f-4903-aa25-5be58625e914","owner":[],"postedDate":"November 21st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[{"id":58323689,"name":"Biological sciences/Cancer"},{"id":58323690,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":58323691,"name":"Physical sciences/Mathematics and computing"},{"id":58323692,"name":"Health sciences/Medical research"},{"id":58323693,"name":"Health sciences/Oncology"}],"tags":[],"updatedAt":"2026-05-15T06:39:13+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-21 02:37:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8157817","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8157817","identity":"rs-8157817","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}