Deep Learning Based Blood Group Detection using Fingerprint

Author(s)	Manasa M, Nandini M, Govardhan E, Deepthi R, M. Narasimha Yadav
Country	India
Abstract	Blood group identification plays a vital role in medical diagnostics, particularly in transfusion services, emergency care, and personalized treatment. Traditional detection methods rely on invasive blood sampling and laboratory procedures, which are often time-consuming and require specialized equipment and trained personnel. To address these challenges, this project introduces a deep learningbased blood group detection system using image analysis. The system supports both invasive and non-invasive identification methods to improve accessibility. It is developed using Python with Flask as the backend framework and a web-based interface for user interaction. Blood sample images are analyzed using a lightweight convolutional neural network for efficient classification. Additionally, a non-invasive approach using fingerprint images is incorporated to reduce dependency on blood collection. Overall, the proposed system enhances speed, accuracy, and usability while offering a modern alternative for healthcare applications.
Keywords	Deep Learning, Fingerprint Analysis, Medical image processing, MobileNetV2
Field	Engineering
Published In	Volume 17, Issue 1, January-June 2026
Published On	2026-04-04
Cite This	Deep Learning Based Blood Group Detection using Fingerprint - Manasa M, Nandini M, Govardhan E, Deepthi R, M. Narasimha Yadav - IJAIDR Volume 17, Issue 1, January-June 2026.

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Journal of Advances in Developmental Research