AFTG-Net: A Deep Attention-based Fusion Framework of Topological and Gradient Features for Pathological Image Analysis

AFTG-Net: A Deep Attention-based Fusion Framework of Topological and Gradient Features for Pathological Image Analysis | 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 AFTG-Net: A Deep Attention-based Fusion Framework of Topological and Gradient Features for Pathological Image Analysis Taymaz Akan, Fatih Gelir, Richa Aishwarya, Md. Shenuarin Bhuiyan, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6710077/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Skeletal muscle pathology is observed by structural disruptions in sarcomeres, increased central nuclei, and changes in myofiber cross-sectional area. In order to classify amyotrophic lateral sclerosis (ALS), diabetes, and healthy controls, pathologists examine the changes in myofiber size using Wheat Germ Agglutinin (WGA) stained histopathological images of various skeletal muscles (quadriceps, gastrocnemius, tibialis anterior, extensor digitorum longus, and soleus). Histological image analysis of skeletal muscle pathology is laborious and subject to inter- and intra-user variability, which can affect diagnosis accuracy and consistency. Conventional techniques like ImageJ-based tools are time-consuming and produce varying outcomes due to their manual cell counting, segmentation, and thresholding. This study introduces AFTG-Net, an attention-based machine learning framework that classifies skeletal muscle histopathological images using complementary geometric and topological descriptors. The model uses globally structural information from Topological Data Analysis (TDA) based on persistent homology and local edge and texture patterns from the Histogram of Oriented Gradients. We suggest a cross-weighted fusion approach that uses cosine similarity to adaptively balance the contributions of these heterogeneous features in order to improve their discriminative power. This integration enables the model to effectively distinguish pathological changes associated with amyotrophic lateral sclerosis (ALS) and Type I diabetes from healthy muscle tissue. We conducted comprehensive comparisons with various state-of-the-art and baseline methods, such as traditional feature-based and deep learning models. We assessed all models by analyzing WGA-stained skeletal muscle images from wild-type and disease models (G93A*SOD1 for ALS and Akita for type 1 diabetes). AFTG-Net outperformed all other models by achieving 92% classification accuracy in distinguishing healthy and diseased muscle fibers. By reducing human intervention, subjectivity, and analysis time, AFTG-Net improves scalability and diagnostic consistency, making it a valuable tool for both biomedical research and clinical practice. Muscle Disease Diagnosis Histogram of Oriented Gradients Topological data analysis Feature Fusion Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 30 Jan, 2026 Reviews received at journal 18 Jul, 2025 Reviews received at journal 14 Jul, 2025 Reviewers agreed at journal 11 Jul, 2025 Reviewers agreed at journal 09 Jul, 2025 Reviewers invited by journal 09 Jul, 2025 Editor assigned by journal 08 Jul, 2025 Editor invited by journal 17 Jun, 2025 Submission checks completed at journal 16 Jun, 2025 First submitted to journal 16 Jun, 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. 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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-6710077","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":482976766,"identity":"c8056559-5065-4c5d-834b-7d077f8e6f75","order_by":0,"name":"Taymaz Akan","email":"","orcid":"","institution":"LSU Health Shreveport","correspondingAuthor":false,"prefix":"","firstName":"Taymaz","middleName":"","lastName":"Akan","suffix":""},{"id":482976767,"identity":"b9be4bd2-37b9-4c6e-a011-1874c591baa1","order_by":1,"name":"Fatih Gelir","email":"","orcid":"","institution":"LSU Health Shreveport","correspondingAuthor":false,"prefix":"","firstName":"Fatih","middleName":"","lastName":"Gelir","suffix":""},{"id":482976768,"identity":"ce77122a-37db-498d-8a78-d517765c5057","order_by":2,"name":"Richa Aishwarya","email":"","orcid":"","institution":"LSU Health Shreveport","correspondingAuthor":false,"prefix":"","firstName":"Richa","middleName":"","lastName":"Aishwarya","suffix":""},{"id":482976769,"identity":"d11b3897-caa4-4ac8-afbc-d28293b9364b","order_by":3,"name":"Md. 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In order to classify amyotrophic lateral sclerosis (ALS), diabetes, and healthy controls, pathologists examine the changes in myofiber size using Wheat Germ Agglutinin (WGA) stained histopathological images of various skeletal muscles (quadriceps, gastrocnemius, tibialis anterior, extensor digitorum longus, and soleus). Histological image analysis of skeletal muscle pathology is laborious and subject to inter- and intra-user variability, which can affect diagnosis accuracy and consistency. Conventional techniques like ImageJ-based tools are time-consuming and produce varying outcomes due to their manual cell counting, segmentation, and thresholding. This study introduces AFTG-Net, an attention-based machine learning framework that classifies skeletal muscle histopathological images using complementary geometric and topological descriptors. The model uses globally structural information from Topological Data Analysis (TDA) based on persistent homology and local edge and texture patterns from the Histogram of Oriented Gradients. We suggest a cross-weighted fusion approach that uses cosine similarity to adaptively balance the contributions of these heterogeneous features in order to improve their discriminative power. This integration enables the model to effectively distinguish pathological changes associated with amyotrophic lateral sclerosis (ALS) and Type I diabetes from healthy muscle tissue. We conducted comprehensive comparisons with various state-of-the-art and baseline methods, such as traditional feature-based and deep learning models. We assessed all models by analyzing WGA-stained skeletal muscle images from wild-type and disease models (G93A*SOD1 for ALS and Akita for type 1 diabetes). 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By reducing human intervention, subjectivity, and analysis time, AFTG-Net improves scalability and diagnostic consistency, making it a valuable tool for both biomedical research and clinical practice.\u003c/p\u003e","manuscriptTitle":"AFTG-Net: A Deep Attention-based Fusion Framework of Topological and Gradient Features for Pathological Image Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-11 11:38:11","doi":"10.21203/rs.3.rs-6710077/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-30T08:04:47+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-18T06:21:00+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-14T10:35:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"41071273860955463231735399477180294369","date":"2025-07-11T08:18:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"67961521570023735534258063531188335939","date":"2025-07-09T07:27:13+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-09T05:59:43+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-08T11:03:35+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-06-17T12:40:02+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-16T16:24:07+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Imaging","date":"2025-06-16T16:20:48+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-imaging","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmim","sideBox":"Learn more about [BMC Medical Imaging](http://bmcmedimaging.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmim/default.aspx","title":"BMC Medical Imaging","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"46baac56-74ac-4c5d-8fea-dd50013491bc","owner":[],"postedDate":"July 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-10T02:38:13+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-11 11:38:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6710077","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6710077","identity":"rs-6710077","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}