Blood group prediction using fingerprint

Blood group prediction using fingerprint | 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 Systematic Review Blood group prediction using fingerprint Kishanjee Kumar, Rohit Prasad, Suman Kumar Ghosh, Sourav Mahanta This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9250543/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The accurate and rapid identification of blood groups is a fundamental prerequisite for medical interventions, including emergency blood transfusions, organ transplantation, and the management of maternal-fetal incompatibilities. Traditional methods of blood typing, primarily based on serological hemagglutination tests, are invasive, requiring venipuncture or finger-pricking, which poses risks of infection, needle-stick injuries, and patient anxiety. Furthermore, these methods are dependent on chemical reagents and trained medical personnel, limiting their accessibility in resource-constrained environments, remote locations, and mass-casualty scenarios. To address these challenges, this project proposes a novel, non-invasive, computer-aided diagnostic system that predicts blood groups by analyzing dermatoglyphic patterns (fingerprints) using advanced Deep Learning and Ensemble Machine Learning techniques. The research leverages the biological correlation between epidermal ridge patterns and blood antigens, both of which are determined during the intrauterine stage of fetal development. To facilitate robust model training, a large-scale dataset comprising 13,932 fingerprint images was utilized. This dataset covers all eight major blood groups (A+, A-, B+, B-, AB+, AB-, O+, O-) and underwent a rigorous preprocessing pipeline. Images were uniformly resized to 128×128 pixels. The image enhancement framework employs a preliminary Gaussian Blur to remove high-frequency sensor artifacts, Contrast Limited Adaptive Histogram Equalization (CLAHE) to improve local ridge clarity, and a secondary Gaussian Blur blending technique to aggressively sharpen local texture details. The core methodology integrates two distinct feature extraction paradigms: hand-crafted texture analysis and automated spatial feature learning. For texture analysis, Histogram of Oriented Gradients (HOG) and Gabor Filters were utilized to capture ridge orientation and frequency information, which were then classified using optimized Random Forest algorithms. Simultaneously, deep spatial features were automatically extracted and learned using a lightweight Custom Convolutional Neural Network (CNN) trained on enhanced grayscale images, and a pre-trained MobileNetV2 architecture leveraging transfer learning on RGB representations. To overcome the limitations of individual classifiers and maximize predictive performance, a robust Stacking Ensemble Model was engineered. This meta-model fuses the probability outputs of all four base classifiers: the Random Forest (HOG), Random Forest (Gabor), Custom CNN, and MobileNetV2 models. A Logistic Regression meta-learner was employed to optimally aggregate these diverse predictions and determine the final blood group class. Experimental evaluation demonstrated that the proposed Stacked Ensemble achieves a superior classification accuracy of 95.59%, significantly outperforming the standalone base models. The ensemble effectively compensated for individual predictive weaknesses, improving upon the accuracies of the HOG + RandomForest (94.58%), Custom CNN (79.69%), Gabor + RandomForest (76.64%), and MobileNetV2 (55.83%) models. The system successfully minimizes misclassification errors, exhibiting strong precision and recall across the varied blood groups. To demonstrate real-world clinical applicability, the end-to-end pipeline was deployed via a user-friendly web interface developed using Streamlit, enabling real-time fingerprint upload, automated image enhancement, and instant, multi-model blood group prediction. This study validates the potential of dermatoglyphics combined with computational intelligence as a reliable, non-invasive supplementary biometric marker for medical diagnostics. Dermatoglyphics Blood Group Prediction Ensemble Machine Learning Deep Learning Non-invasive Diagnostics Convolutional Neural Networks (CNN) Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted 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-9250543","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Systematic Review","associatedPublications":[],"authors":[{"id":615626044,"identity":"9ec23e63-8fa9-4af9-9798-1f7331db0faf","order_by":0,"name":"Kishanjee Kumar","email":"data:image/png;base64,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","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Kishanjee","middleName":"","lastName":"Kumar","suffix":""},{"id":615626045,"identity":"60989cde-8747-4e28-a514-e9046a318d2b","order_by":1,"name":"Rohit Prasad","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Rohit","middleName":"","lastName":"Prasad","suffix":""},{"id":615626046,"identity":"158df0fe-ff2a-42dd-ad9f-afde7c423159","order_by":2,"name":"Suman Kumar Ghosh","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Suman","middleName":"Kumar","lastName":"Ghosh","suffix":""},{"id":615626047,"identity":"7ea44103-dc3e-48ab-9764-3f5866127d85","order_by":3,"name":"Sourav Mahanta","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Sourav","middleName":"","lastName":"Mahanta","suffix":""}],"badges":[],"createdAt":"2026-03-28 07:23:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9250543/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9250543/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106639855,"identity":"5789f9fb-3b3e-478e-b018-2b854a041943","added_by":"auto","created_at":"2026-04-10 17:40:50","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":983442,"visible":true,"origin":"","legend":"","description":"","filename":"Paper.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9250543/v1_covered_127f884a-a0c1-4483-b7e3-4be1376a71f5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Blood group prediction using fingerprint","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Dermatoglyphics, Blood Group Prediction, Ensemble Machine Learning, Deep Learning, Non-invasive Diagnostics, Convolutional Neural Networks (CNN)","lastPublishedDoi":"10.21203/rs.3.rs-9250543/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9250543/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe accurate and rapid identification of blood groups is a fundamental prerequisite for medical interventions, including emergency blood transfusions, organ transplantation, and the management of maternal-fetal incompatibilities. Traditional methods of blood typing, primarily based on serological hemagglutination tests, are invasive, requiring venipuncture or finger-pricking, which poses risks of infection, needle-stick injuries, and patient anxiety. Furthermore, these methods are dependent on chemical reagents and trained medical personnel, limiting their accessibility in resource-constrained environments, remote locations, and mass-casualty scenarios. To address these challenges, this project proposes a novel, non-invasive, computer-aided diagnostic system that predicts blood groups by analyzing dermatoglyphic patterns (fingerprints) using advanced Deep Learning and Ensemble Machine Learning techniques.\u003c/p\u003e \u003cp\u003eThe research leverages the biological correlation between epidermal ridge patterns and blood antigens, both of which are determined during the intrauterine stage of fetal development. To facilitate robust model training, a large-scale dataset comprising 13,932 fingerprint images was utilized. This dataset covers all eight major blood groups (A+, A-, B+, B-, AB+, AB-, O+, O-) and underwent a rigorous preprocessing pipeline. Images were uniformly resized to 128\u0026times;128 pixels. The image enhancement framework employs a preliminary Gaussian Blur to remove high-frequency sensor artifacts, Contrast Limited Adaptive Histogram Equalization (CLAHE) to improve local ridge clarity, and a secondary Gaussian Blur blending technique to aggressively sharpen local texture details.\u003c/p\u003e \u003cp\u003eThe core methodology integrates two distinct feature extraction paradigms: hand-crafted texture analysis and automated spatial feature learning. For texture analysis, Histogram of Oriented Gradients (HOG) and Gabor Filters were utilized to capture ridge orientation and frequency information, which were then classified using optimized Random Forest algorithms. Simultaneously, deep spatial features were automatically extracted and learned using a lightweight Custom Convolutional Neural Network (CNN) trained on enhanced grayscale images, and a pre-trained MobileNetV2 architecture leveraging transfer learning on RGB representations.\u003c/p\u003e \u003cp\u003eTo overcome the limitations of individual classifiers and maximize predictive performance, a robust Stacking Ensemble Model was engineered. This meta-model fuses the probability outputs of all four base classifiers: the Random Forest (HOG), Random Forest (Gabor), Custom CNN, and MobileNetV2 models. A Logistic Regression meta-learner was employed to optimally aggregate these diverse predictions and determine the final blood group class.\u003c/p\u003e \u003cp\u003eExperimental evaluation demonstrated that the proposed Stacked Ensemble achieves a superior classification accuracy of 95.59%, significantly outperforming the standalone base models. The ensemble effectively compensated for individual predictive weaknesses, improving upon the accuracies of the HOG\u0026thinsp;+\u0026thinsp;RandomForest (94.58%), Custom CNN (79.69%), Gabor\u0026thinsp;+\u0026thinsp;RandomForest (76.64%), and MobileNetV2 (55.83%) models. The system successfully minimizes misclassification errors, exhibiting strong precision and recall across the varied blood groups. To demonstrate real-world clinical applicability, the end-to-end pipeline was deployed via a user-friendly web interface developed using Streamlit, enabling real-time fingerprint upload, automated image enhancement, and instant, multi-model blood group prediction. 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