BLOOD GROUP DETECTION USING DEEP LEARNING AND ANALYSIS OF MEDIUM SCALE DATASET OF FINGERPRINT SAMPLES

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G.ANITHA CHOWDARY; B.PRASANNA LAXMI; B.AKHILA; A.NATARAJ

BLOOD GROUP DETECTION USING DEEP LEARNING AND ANALYSIS OF MEDIUM SCALE DATASET OF FINGERPRINT SAMPLES

Authors: G.ANITHA CHOWDARY, B.PRASANNA LAXMI, B.AKHILA, A.NATARAJ

Unique Paper ID: 178278
Volume: 11
Issue: 12
PageNo: 4236-4241

Keywords: Non-invasive diagnostics Fingerprint biometrics Blood group prediction Deep learning CNN ResNet DenseNet Medical image processing

Abstract:
This project proposes a non-invasive method for predicting human blood groups using fingerprint images, harnessing the power of deep learning architectures such as CNN, ResNet, and DenseNet. Traditional serological methods for blood group determination are invasive, reliant on lab facilities, and time-consuming—making them unsuitable in emergency and remote scenarios. By using unique and stable fingerprint patterns as input, the system offers a contactless, rapid, and scalable diagnostic alternative. The deep learning models are trained on labeled fingerprint datasets to identify features that correlate with blood group types. The proposed solution demonstrates promising accuracy, indicating its potential use in medical diagnostics, particularly in resource-constrained environments.

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ISSN: 2349-6002
Volume: 11
Issue: 12
PageNo: 4236-4241

BLOOD GROUP DETECTION USING DEEP LEARNING AND ANALYSIS OF MEDIUM SCALE DATASET OF FINGERPRINT SAMPLES

Available:https://ijirt.org/article?manuscript=178278

Impact Factor
8.01 (Year 2024)

UGC Approved
Journal no 47859

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