AI-Driven Blood Type Image Recognition System: Medical Diagnostic Application of Deep Learning and Firefly Optimization Algorithm

Original Author/Maintainer: Kella Kushmitha Source Platform: GitHub Original Title: AI-driven-classification-prediction-of-blood-group-through-image-processing Original Link: https://github.com/KellaKushmitha/AI-driven-classification-prediction-of-blood-group-through-image-processing Publish Time: May 23, 2026

This project presents an automated blood type classification system based on image processing and deep learning, which combines the Firefly Optimization Algorithm (FFO) to enhance model performance. It achieves an accuracy rate of over 98% in blood sample image analysis, providing a fast, efficient, and scalable solution for medical diagnosis.

Blood type identification is a key clinical testing item. Traditional methods rely on manual interpretation, which is time-consuming and prone to errors due to human factors. In emergency medical scenarios (such as massive bleeding rescue, battlefield care) and resource-poor remote areas, fast and accurate blood type identification is critical to patient safety. With the deepening application of AI in the medical field, automated diagnostic systems based on computer vision and deep learning have become a development direction, leading to the birth of this project.

The system uses a multi-layer technology stack: OpenCV is used for image preprocessing (noise removal, contrast enhancement, etc.); the core classifier is a Deep Neural Network (DNN), with end-to-end learning to avoid manual feature engineering; the Firefly Optimization Algorithm (FFO) is introduced to optimize network weights and biases, helping to escape local optima.

Workflow: User uploads image via Web → Django backend processing → Preprocessing (size adjustment, grayscale conversion, etc.) → Convolutional layer feature extraction → DNN classification to output ABO blood type → Result presentation on Web, fully automated.

Tech stack: Python programming language; Django for backend construction; TensorFlow/Keras for deep learning; OpenCV for image processing; HTML/CSS for frontend. The selection balances development efficiency and performance.

Application scenarios: Hospital laboratories to assist in improving efficiency; rapid screening at emergency rescue sites; lowering detection thresholds in remote areas; integrating with medical systems to connect to HIS/LIS for automatic medical record entry.

The system's accuracy exceeds 98%, meeting clinical needs. Advantages: Fast speed (completed in seconds), scalable (easy to deploy), objective (no subjective interference).

Limitations: Brief documentation (lack of dataset description, etc.); need to address regulatory compliance and clinical validation issues. Future directions: Expand dataset, model lightweighting, multi-modal fusion diagnosis.