AI-Powered Blood Group Detection Using Computer Vision and Transfer Learning

AI-Powered Blood Group Detection Using Computer Vision and Transfer Learning Introduction Artificial Intelligence is increasingly being explored for healthcare applications where computer vision and machine learning can assist with diagnostic and analytical workflows. One area of interest is the automated analysis of blood agglutination patterns for blood group detection. My research work focuses on developing an affordable, image-based blood group detection approach that combines Computer Vision, Transfer Learning, and mobile-optimized deep neural networks. The broader objective is to investigate how AI can support low-cost healthcare automation while maintaining a confidence-aware approach to classification. Research Approach The system is designed around image-based analysis of blood agglutination patterns. Instead of relying only on conventional manual interpretation, the research investigates how deep learning can extract meaningful visual features from blood reaction images and use them for automated classification. A major component of the approach is Transfer Learning, where a pre-trained convolutional neural network is used as a feature-extraction backbone. This allows the system to leverage learned visual representations while reducing the computational requirements associated with training a deep model entirely from scratch. The extracted features are then used within a calibrated classification pipeline for predictive analysis. Confidence-Aware Classification An important aspect of this research is that the system is not designed around classification alone. The research incorporates a confidence-aware classification pipeline using a frozen CNN backbone for feature extraction followed by calibrated tabular classifiers. This approach is intended to distinguish between predictions that can be handled with higher confidence and cases that may require additional clinical review. Such a strategy is particularly relevant in healthcare-oriented AI systems, where an uncertain prediction should not necessarily be treated in the same way as a highly confident prediction. Dataset Development Research in computer vision depends heavily on the quality and structure of the underlying data. As part of this work, I curated the Blood Agglutination Curated Dataset (V1), a quality-controlled binary classification dataset developed for automated blood group agglutination analysis. The dataset has been published on Zenodo as a research resource and is intended to support further experimentation in biomedical computer vision and healthcare AI. DOI: 10.5281/zenodo.21384197 From Research to a Practical System The research also extends beyond software experimentation. The work is connected with an AI-powered blood group detection kit designed around an affordable and image-based approach. The associated invention incorporates a biodegradable design and AI analytics. A patent application titled “Dual-Mode Blood Group Testing Device Featuring Biodegradable Design and AI Analytics” was filed under application number 202511125188 and is listed as published. This provides an example of how an AI research concept can progress from a computational model toward a broader technology and device-oriented research direction. Research Publication The research has also been documented through the publication: “Automated Blood Group Detection Using Computer Vision and Transfer Learning on Mobile-Optimized Deep Neural Networks” The work covers the intersection of Artificial Intelligence, Machine Learning, Computer Vision, Healthcare AI, Transfer Learning, and Deep Neural Networks. DOI: 10.5281/zenodo.20224963 Future Direction The current work represents an ongoing research direction rather than a finished clinical system. Future development can focus on improving dataset diversity, evaluating model robustness, studying confidence calibration, optimizing inference for resource-constrained environments, and conducting more extensive validation. The broader goal is to explore how responsible AI engineering can contribute to accessible healthcare technologies while keeping model limitations and uncertain predictions visible. Conclusion AI-assisted blood group detection demonstrates how Computer Vision and Machine Learning can be applied to a practical healthcare problem. By combining image-based analysis, Transfer Learning, confidence-aware classification, dataset development, and device-oriented innovation, this research brings together several areas of my work in Artificial Intelligence and Healthcare Technology. The project is part of my broader interest in developing scalable AI systems that connect research, software engineering, and real-world applications.