DETECTION OF BLOOD CELL IN HUMAN BLOOD SAMPLES USING MICROSCOPIC IMAGES

Computer Science

Accurate and efficient analysis of blood cell populations is crucial for diagnosing various medical conditions and monitoring overall health. This project presents a novel approach to automate the detection and quantification of blood cells in human blood samples using microscopic images. Leveraging advanced image processing and machine learning techniques, this research aims to revolutionize haematological analysis by reducing human intervention and increasing diagnostic precision. The proposed system employs image segmentation to isolate individual blood cells from complex microscopic images, followed by classification into distinct cell types (e.g., red blood cells, white blood cells, and platelets). Utilizing deep learning models, the algorithm learns to recognize subtle variations in cell morphology and staining patterns, achieving remarkable accuracy in cell identification and counting. This technology promises to expedite blood cell analysis, making it more accessible and cost-effective while minimizing the risk of human error. Furthermore, it has the potential to advance medical research, improve disease diagnosis, and enhance patient care. By seamlessly integrating advanced image processing and machine learning techniques, this study seeks to transform haematological analysis by reducing manual intervention and elevating diagnostic precision. The proposed system utilizes YOLOv8's robust object detection capabilities to precisely identify and count individual blood cells from intricate microscopic images, ultimately enhancing the efficiency and accuracy of blood cell analysis. This project represents a significant step towards harnessing the power of modern image analysis and artificial intelligence for the benefit of healthcare and biomedical sciences.

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