Abstract

Blood group detection is a crusial aspect of medical diagnostics, widely used in blood transfusions, organ transplants, and prenatal care. Traditional blood typing methods require blood samples and reagents, which can be invasive, time- consuming, and costly. This research proposes a novel, non-invasive approach to identify blood group using fingerprint analysis. By using Cutting-edge techniques for image processing , we analyse unique fingerprint patterns and identify specific features linked to blood types. Our methodology incorporates image enhancement, Extracting data attributes and employing learning algorithms to classify blood groups accurately. We are planning to optimize and check the performance of the model on a robust dataset, achieving promising correctness and performance in blood group prediction. This technique holds promise to redefine blood typing processes, providing a rapid, cost-effective, and accessible solution for various medical applications, particularly in remote or resource-limited areas. Further improvements in accuracy through larger datasets and advanced algorithms could establish fingerprint-based blood group detection as a viable alternative to conventional methods.

Introduction

I. INTRODUCTION

Accurate blood group identification is fundamental in healthcare, serving a vital role in transfusion medicine, organ transplantation, and trauma care. Conventional blood typing methods rely on laboratory-based testing, requiring blood samples and specific reagents, which may not be readily available in low-resource settings or emergency situations. Consequently, there is a growing interest in developing non-invasive, rapid blood group detection techniques that are accessible and do not rely on laboratory infrastructure.

Fingerprint-based blood group detection is a novel approach that builds on the well-established field of biometrics. Fingerprints unique patterns and their stability over a person’s lifetime, making them an ideal candidate for personal identification. Fascinatingly, new studies propose that certain fingerprint features may correlate with genetic and physiological characteristics, including blood group. This potential link provides the basis for using fingerprint patterns as an indirect biomarker for blood type classification, offering a path toward non-invasive diagnostic tools. The primary challenge in this approach lies in accurately extracting and analyzing fingerprint features that are potentially indicative of blood groups. Feature extraction, therefore, is fundamental to transforming raw fingerprint images into meaningful data. Established image processing approaches like Scale-Invariant Feature Transform (SIFT), Histogram of Oriented Gradients (HOG), and Local Binary Patterns (LBP), have shown promise in capturing key textural and edge-based characteristics within fingerprint patterns. SIFT, for instance, is robust to scale and rotation, enabling it to identify consistent features across different images. focuses on edge directions and gradients, making it effective in capturing the ridge and valley patterns in fingerprints, while LBP is advantageous for texture classification, which is capable of being useful for differentiating the subtle variations linked to blood types. In addition to these classical techniques, recent advancements in deep learning have advanced (CNN)- based methods that can automatically learn high-level features from fingerprint images without manual engineering. These deep learning techniques, through layer-wise feature extraction, can capture intricate details and complex relationships that may be challenging to discern with traditional methods. By combining both conventional and deep learning feature extraction techniques, we aim to build a robust pipeline for analyzing fingerprint visualize data in the context of blood group prediction. Subsequent to feature extraction, ML algorithms are served to classify the fingerprints into specific blood groups. We conduct an extensive evaluation of both extracted features and classification models to determine the best strategy for this task. The proposed methodology could potentially transform blood group detection, offering a cost-effective, non-invasive solution that could be deployed on mobile devices for on-the-spot diagnosis. Our research  promotes the progress of novel diagnostic tools in the biomedical imaging domain by exploring an innovative application of fingerprint patterns. Results have significance for mobile health (mHealth) applications, emergency medical services, and rural healthcare systems, where rapid, non-invasive blood typing can enhance patient care outcomes and accessibility.

II. LITERATURE REVIEW

Linking Fingerprint Features to Blood Groups for Forensic Applications (Yadav et al., 2021) This research from India investigates the correlation between fingerprint patterns and blood groups for forensic purposes. The authors observed distinctive variations in fingerprint features—such as ridge configurations and loops—across different blood groups (A, B, AB, O). They utilized a machine learning algorithm, specifically the Support Vector Machine (SVM), to predict blood types based on fingerprint characteristics. Their model achieved a classification accuracy of 85%, demonstrating the feasibility of using fingerprint data for blood group identification in forensic and emergency situations.

Using Convolutional Neural Networks for Blood Group Prediction from Fingerprints (Kumar & Singh, 2022) In this study, researchers from India employed Convolutional Neural Networks (CNNs) to predict blood groups by analyzing fingerprint images. After enhancing and normalizing the fingerprint images, the CNN model was trained to learn relevant features autonomously, eliminating the need for manual feature extraction. The deep learning approach achieved a classification accuracy of 90.5%, proving that CNNs can effectively automate blood group detection using fingerprints.

Application of Machine Learning for Blood Group Classification Using Fingerprint Data (Patel & Verma, 2020)

This study explores the use of traditional machine learning techniques like Random Forest, K-Nearest Neighbors, and Decision Trees for classifying blood groups based on fingerprint features. The researchers focused on minutiae points, ridge counts, and fingerprint loops. Among the models tested, Random Forest achieved the highest accuracy of 88.3%, underscoring the importance of selecting the right features and refining the dataset for improved classification performance.

Data Augmentation to Enhance Fingerprint-Based Blood Group Prediction (Gupta & Sharma, 2024) This Indian study investigates the effectiveness of data augmentation in improving machine learning models for predicting blood groups from fingerprint images. Techniques such as rotation, scaling, and flipping were applied to expand the fingerprint dataset, thus preventing overfitting and boosting the model’s generalization ability. A Convolutional Neural Network (CNN) was trained using this augmented data, leading to an impressive 93.2% accuracy, illustrating the role of data augmentation in enhancing the performance of biometric- based classification systems.

Deep Learning Approaches for Blood Group Prediction from Fingerprints (Zhang et al., 2023) A study from China explores the use of hybrid deep learning models, combining Convolutional Neural Networks (CNNs) with Long Short-Term Memory (LSTM) networks to predict blood groups from fingerprint data. The hybrid model captures both the spatial and temporal features of fingerprint patterns, achieving a 92% accuracy. This approach demonstrates that combining different deep learning architectures can improve the accuracy of blood group classification from biometric data.

Combining Feature Extraction and Machine Learning for Blood Group Classification (Mohamed & Ahmad,2019) Researchers from the UAE developed a hybrid methodology that integrates Local Binary Patterns (LBP) for feature extraction with machine learning algorithms like Gradient Boosting Machines (GBM) to classify blood groups from fingerprints. The combination of handcrafted feature extraction and machine learning resulted in an accuracy of 87%, suggesting that integrating these approaches can yield  better performance in biometric-based identification systems.

Multi-Biometric Systems for Blood Group Detection (Patel et  al.,2022) This study from the United Kingdom investigates the potential of multi-biometric systems to improve blood group detection. By combining fingerprint data with other biometric traits, such as iris scans, researchers developed a multi-stream deep learning model that processes various biometric features simultaneously. This approach yielded a 95% accuracy, suggesting that multi-modal systems could offer more reliable results in real-world applications like forensic analysis or medical diagnostics.

Developing a Global Fingerprint Database for Blood Group Classification (Lee et al., 2022) This international collaboration, involving researchers from the United States, South Korea, and Germany, created a comprehensive global fingerprint database linked to blood types. With over 10,000 fingerprint samples, the team used CNN-based models to predict blood groups. The resulting model achieved a high accuracy of 97%, emphasizing the significance of large, diverse datasets for creating robust and scalable blood group prediction systems based on fingerprint data.

Optimizing Blood Group Prediction from Fingerprints Using Genetic Algorithms (Gomez et al., 2023) In this study, researchers from Spain combined genetic algorithms with traditional machine learning techniques such as Random Forest and SVM for blood group classification from fingerprint data. The optimization of feature selection using genetic algorithms improved the classification accuracy to 91%, highlighting the potential of hybrid approaches for boosting the effectiveness of biometric-based prediction systems.

A Review of Biometric Approaches for Blood Group Identification (Wang et al., 2021) This review paper, published in Japan, provides an overview of various biometric methods used for blood group classification, including fingerprints, palm prints, and iris scans. It assesses the effectiveness of different machine learning models, including deep learning techniques like CNNs, as well as Support Vector Machines (SVM) and K-Nearest Neighbors (KNN). The review also highlights challenges such as dataset limitations, class imbalance, and the standardization of data collection methods. It concludes with suggestions for future research that could involve integrating multiple biometric features to enhance the accuracy and reliability of blood group classification systems.

III. METHODOLOGY

A. Introduction

The project method is created to lay out a strategy for identifying blood types using fingerprint examination with the help of machine learning-based image processing methods. The objective of this method is to design a precise classification system that doesn't require invasion by utilizing fingerprint traits that could be linked to blood types.

B. Hardware and Software requirement

1) Hardware requirement

Fingerprint Scanner: Acquiring fingerprint images in crucial resolution is crucial for determining ridge count and minutiae points, along with texture-related features that play a role in blood group categorization.

2) Software requirement

1. OpenCV: Essential for preparing fingerprint images, for analysis purposes like ridge count and minutiae point extraction processes  require preprocessing  steps  such,  as enhancement techniques and segmentation followed by region of interest (ROI) extraction.
2. NumPy: Used for Data handling which is frequently employed in the process of extracting features.
3. Pandas: Crucial, for structuring and handling data effectively with feature sets to facilitate data retrieval for machine learning purposes.
4. Mahotas: In image processing methods like transformations and extracting texture features, through GLCM (Gray Level Co?occurrence Matrix).
5. Matplotlib and Seaborn: This helps visualize the feature value distributions, the significance of each feature, and the correlations between the extracted features.

Table 1. Feature Ranges for Blood Group Classification Based on Fingerprint Analysis

C. Procedure

1. Data Collection: High-quality fingerprint images were acquired through a fingerprint scanner. These images were pre-processed for noise reduction and normalization to ensure clarity and uniformity.
2. Image Preprocessing: Advanced image processing techniques were utilized to enhance fingerprint images , extracting features such as ridges and minutiae points.
3. Feature Extraction: The following features were identified and extracted from the fingerprint images:

• Ridge Count: The number of ridges in the fingerprint was counted, focusing on specific regions of interest (ROI).
• Minutiae Points: Points where ridges end or bifurcate were identified and extracted.
• Gray-Level Co-occurrence Matrix (GLCM): Statistical features from the GLCM were extracted to characterize the texture patterns in the fingerprint images.

1. Blood Group Classification: A machine learning model was developed and trained to classify fingerprints into their corresponding blood groups using the extracted features. To evaluate the model's accuracy the dataset was split into training and testing subsets.
2. Model Evaluation: The model was evaluated using accuracy , precision , recall and a confusion matrix .

D. Techniques and Algorithms

1) Image Enhancement Techniques

1. Histogram Equalization: Improves the overall contrast of the image, making the fingerprint ridges and valleys more distinguishable.
2. Gaussian Smoothing: Used for reducing noise present in the image.
3. Gabor Filtering: Enhances the ridges by focusing on specific frequencies and orientations that correspond to the ridges characteristics.
4. Adaptive Thresholding: Create a binary image.

• Morphological Operations: Refine ridges and reduce noise.
• Fourier Transform: Enhance periodic patterns.

2) Feature Extraction Techniques

1. Correlation-Based Feature Selection (CFS) using a Genetic Algorithm (GA):

• First, extract various features from fingerprint images (ridge counts, minutiae points, texture, orientation).
• Define the Target Variable: Blood group is your target class (A, B, AB, O)
• Use the correlation-based feature selection to evaluate each feature's relevance to the blood group and redundancy with other features.

1. VGG16

• Load the pre-trained VGG16 model from ImageNet .
• pre-trained VGG16 is for general image classification, remove the fully connected (FC) layers (top layers) and replace them with a custom classification head suitable for blood group detection.

E. System Architecture

Fig 1. Fingerprint Patterns Associated with Different Blood Groups

Conclusion

The project focused on blood group prediction using fingerprint analysis through advanced machine learning techniques offers a promising step forward in biometric- based medical diagnostics. By integrating fingerprint data with powerful classification algorithms, this system aims to provide quick, non-invasive, and accurate identification of blood groups, offering a practical solution for various applications in forensics, emergency medicine, and medical record management. This approach holds significant potential, providing advantages such as high accuracy, efficiency, and ease of use. However, challenges remain, particularly regarding dataset variability, the need for large-scale data, and potential technical limitations in feature extraction from fingerprint images. Addressing these challenges will be crucial for maximizing the system\'s capabilities and ensuring its broader applicability across diverse populations.

References

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Copyright

Copyright © 2024 Vidya Waykule, Sakshi Amrutkar, Om Jadhav, Vardhman Jain, Titiksha Jawale. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.