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krishnakantlodhi/
blood-group-fingerprint-simple

Name: blood-group-fingerprint-simple
Availability: InStock

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Simple blood-group fingerprint classifier (single-file)

One Python file that runs the whole classical-ML pipeline end to end:

load BMPs  ->  preprocess (CLAHE + Gabor)  ->  features (HOG/LBP/GLCM)
            ->  stratified 70/15/15 split  ->  train 6 classifiers
            ->  evaluate on held-out test  ->  save models + results.json

Run

bash

pip install -r requirements.txt
python main.py

First run takes ~3 minutes (preprocesses + extracts features for all 6,000 images, then trains six models). Subsequent runs reuse the feature cache and finish in ~1 minute.

Outputs

cache/features.npy, cache/labels.npy — the cached feature matrix
models/<name>.pkl — six trained sklearn pipelines
results.json — leaderboard with per-class precision/recall/F1

Models trained

Model	What it is
logistic_regression	linear, scaled, class-balanced
linear_svm	LinearSVC + Platt calibration (predict_proba)
knn_pca50	KNN (k=11, distance-weighted) on PCA-50
random_forest	500 trees, class-balanced
gaussian_nb	scaled GaussianNB
decision_tree	max_depth=20, min_leaf=5, class-balanced

Honest caveat

The dataset's filenames are cluster_N_*.BMP where N matches the class — strong evidence the labels were generated by clustering rather than real ABO+Rh blood typing. A linear model on handcrafted features matches a deep network on this data, which means both are fitting a dataset artifact rather than biology. Do not use this for medical decisions.

krishnakantlodhi/blood-group-fingerprint-simple

Simple blood-group fingerprint classifier (single-file)

Run

Outputs

Models trained

Honest caveat

krishnakantlodhi/
blood-group-fingerprint-simple