DEEP LEARNING BASED BLOOD BANK

Abstract: Blood bank management plays a vital role in modern healthcare systems, where timely availability of safe and sufficient blood can save lives during emergencies, surgeries, and critical treatments. However, traditional blood bank systems often rely on manual processes or basic digital systems that lack intelligence and predictive capabilities. These limitations can lead to serious issues such as blood shortages, overstocking, and wastage due to the limited shelf life of blood units. To address these challenges, this project proposes a Deep Learning Based Blood Bank Management System that combines efficient data management with intelligent forecasting techniques. The primary objective of the proposed system is to automate blood bank operations while incorporating advanced predictive analytics to improve decision- making. The system is developed using Python as the core programming language, with FastAPI used for backend API development and Uvicorn as the server for handling asynchronous requests. The frontend is built using HTML and Flask for rendering dynamic web pages, providing a simple and user-friendly interface for both administrators and users. The database is managed using SQLite, which stores all essential data such as donor details, blood inventory, and request records. A key feature of this system is the integration of a deep learning model for forecasting blood demand. Using historical data, the model applies time-series analysis techniques such as Long Short-Term Memory (LSTM) networks to predict future blood requirements. This predictive capability enables blood banks to maintain optimal stock levels, reduce wastage, and ensure availability during peak demand periods or emergencies. The system also includes modules for donor registration, blood stock management, request handling, and administrative monitoring, all accessible through a centralized web interface. The proposed system significantly enhances the efficiency and reliability of blood bank operations by reducing manual errors and enabling real-time data access. It supports faster decision-making, improves resource utilization, and contributes to better healthcare service delivery. Although the system is designed for small to medium-scale deployment using SQLite, it can be easily scaled to larger environments by integrating advanced databases and cloud-based infrastructure.

Keywords: Blood Bank Management, Deep Learning, LSTM, FastAPI, Uvicorn, SQLite, Flask, Blood Demand Forecasting, Healthcare Automation, Python.