Bibliographic record
Abstract
The increased demand for speed and accuracy in medical diagnosis has motivated the use of artificial intelligence techniques in the healthcare domain. There is an urgent need to take necessary steps for developing non-invasive and reliable systems to assist in diagnosis. The main aim of this work is to identify and implement modern artificial intelligence methods to speed up and increase the accuracy of blood group detection by using fingerprint images. In order to achieve higher accuracy, a deep learning-based approach of Convolutional Neural Networks (CNNs) has been incorporated and tested in this work. Several fingerprint parameters like ridge flow, ridge density, and minutiae points are considered in this work. In this project, a fingerprint-based dataset comprising different samples of blood groups has been used, as the accurate detection of blood groups plays a very important role in different medical emergencies and healthcare applications. The added advantage of the system is its non-invasive and time-saving nature, making it an effective alternative to traditional blood testing procedures. A module has been designed that predicts an individual's blood group by analysing their fingerprint image. This module focuses more on predictive analytics than invasive diagnostics, with a greater emphasis on automation and accessibility in the healthcare domain. The designed architecture will contribute to other AI-based biometric diagnosis systems, and it provides a concrete base for further medical image analysis and artificial intelligence applications. In addition to blood group identification, the system also includes a module for detecting the presence of diabetes based on fingerprint image features, making it useful for early health screening.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".