Enhancing Hematology Blood Cell Detection and Quantification through the Use of YOLOv8 in Python

Project Synopsis & Overview

This project presents an AI-based approach for blood cell detection and counting in hematology using the YOLOv8 object detection algorithm. The system addresses the limitations of traditional blood cell analysis methods, including manual microscopic examination, high labor requirements, time consumption, subjectivity, dependency on skilled personnel, and difficulties caused by morphological variations. The proposed system uses digital blood sample images acquired through microscopes or digital cameras as input and applies image preprocessing techniques such as image normalization, resizing, and augmentation to prepare standardized data for analysis. A labeled dataset of blood cell images is prepared and divided into training, validation, and testing subsets. The YOLOv8 architecture is configured and trained using the prepared dataset to identify and differentiate various blood cell types. The major modules include the Image Input Module, Preprocessing Module, YOLOv8 Architecture Module, Dataset Preparation Module, Training Module, Object Detection Module, and Counting Module. The detection module produces bounding boxes and class labels for individual blood cells, while the counting module quantifies the detected cells. The expected output is automated blood cell detection, classification, and counting with visualized results. The system can support hematology diagnostics, clinical laboratories, hospitals, medical research, and educational applications.

Complete Technical Specifications