krishna111809/fingerprint-based-blood-group-detection
This repository presents an innovative approach to classifying blood groups using fingerprint images through deep learning techniques. The project explores state-of-the-art convolutional neural network (CNN) architectures, such as ResNet, VGG16, AlexNet, and LeNet, to analyze and predict blood groups accurately.
This project helps medical and forensic professionals classify human blood groups from fingerprint images. You input a fingerprint image, and the system outputs a predicted blood group (e.g., A+, O-). This tool is designed for laboratory technicians, forensic scientists, or researchers in medical imaging.
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Use this if you need an automated method to predict blood groups from fingerprint scans, potentially for rapid screening or forensic analysis.
Not ideal if you require 100% diagnostic certainty for clinical decisions, as this is an experimental machine learning approach.
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Jupyter Notebook
License
MIT
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Last pushed
Nov 12, 2024
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