BloodAI Pro: A Hybrid Deep Learning and Computer Vision Approach for Automated Leukemia Detection using Microscopic Blood Smears

Abstract: Leukemia, a severe form of blood cancer, requires immediate diagnosis and early intervention for higher survival rates. Traditional diagnostic methods involve the manual examination of blood smear slides under a microscope by expert pathologists, which is time-consuming, expensive, and prone to human error due to fatigue. In rural and resource-limited areas, the lack of digital scanners and specialized hematologists heavily delays the diagnosis. This paper proposes "BloodAI Pro," an automated, cost-effective "Lab-on-a-Phone" diagnostic system. The proposed system integrates a traditional microscope with a smartphone interface, utilizing a Hybrid Ensemble technique that combines Computer Vision and Deep Learning. A Convolutional Neural Network (CNN) trained on the C-NMC dataset forms the core prediction engine, while a custom mathematical Image Processing algorithm calculates Cell Density (Hypercellularity) to eliminate False Positives. Furthermore, the system is backed by a robust FastAPI server and automatically generates NABL-standard diagnostic PDF reports. The results demonstrate high precision, an extremely low False Negative rate, and the ability to classify the severity of the disease based on clinical cell-crowding logic.

Keywords: Leukemia Detection, Convolutional Neural Networks (CNN), Deep Learning, Image Processing, Hypercellularity, FastAPI, Computer Vision, Lab-on-a-Phone.