Blood Group Detection via Finger Print Analysis on Raspberry PI

Abstract
When it comes to medical emergencies and diagnostics, blood group detection is very crucial. It may be time-consuming, resource-intensive, and inaccessible in rural places to conduct laboratory-based tests for blood type identification, which are the traditional approaches. The purpose of this research is to investigate a new method of blood group identification that is based on fingerprint analysis and is driven by machine learning. The method is implemented on a portable and inexpensive Raspberry Pi platform. The suggested method for capturing and preprocessing fingerprint photographs makes use of state-of-the-art image processing algorithms. Features that are associated with certain blood group traits are derived from fingerprint patterns. These characteristics are used to properly predict the person's blood type via a machine learning model that has been trained on a varied dataset. The system may be easily deployed in field contexts, rural healthcare clinics, and settings with limited resources thanks to the usage of Raspberry Pi, which allows for real-time processing and is portable. In comparison to more traditional approaches, the experimental findings show that fingerprint-based blood group identification is feasible and achieves comparable accuracy. The Raspberry Pi guarantees affordability and scalability, while the use of machine learning techniques guarantees resilience and flexibility. Keywords - Computer Vision, Image Processing, Fingerprint Analysis, Machine Learning, Blood Group Detection Medical Technology, Non-Invasive Diagnostics, Raspberry Pi, and Embedded Systems