rohit-kandelkar/ICATES-2025-Blood-Group-Paper
Paper and presentation from ICATES 2025: A non-invasive, deep learning approach to detect blood groups from fingerprints. Our Shallow CNN model achieves 89.49% accuracy, offering a fast, cost-effective alternative to traditional methods.
Stars
—
Forks
—
Language
—
License
—
Category
Last pushed
Oct 18, 2025
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/rohit-kandelkar/ICATES-2025-Blood-Group-Paper"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
Michael-OvO/Skin-Burn-Detection-Classification
Highly Accurate and Efficient Burn detection and Classification trained with Deep Learning Model
Tirth27/Skin-Cancer-Classification-using-Deep-Learning
Classify Skin cancer from the skin lesion images using Image classification. The dataset for the...
ImageMarkup/isic-archive
International Skin Imaging Collaboration: Melanoma Project
aevri/mel
Tools to help identify new and changing moles on the skin with the goal of early detection of...
samhaswon/skin_segmentation
This repository details the various methods I have attempted for skin segmentation.