Blood Cell Detection and Count
Keywords:
YOLOV10, blood smear images, blood cell classification, blood cell types, diagnosis.Abstract
A blood cell count is crucial for both diagnosing and treating a wide range of illnesses. This work suggests and puts into practice a thorough method for analysing blood cell, utilizing advanced neural network-based structures for both counting blood cells and classifying blood cell types, that are vital to assess an individual's overall health status. One of the most frequent examinations carried out in medical treatment facilities is the counting of blood cells. Conventional laboratory methods for measuring blood cells are time-consuming and difficult. Because humans are involved in this complex process, they may produce erroneous results. In this work, we suggest a system for automated blood cell counting that uses a real-time object detection algorithm YOLO (You Only Look Once) of version 10 (yolov10), which is used instead of Convolutional Neural Network (CNN) to conquer over CNN for faster and accurate performance. Microscopic images of blood smears are used to identify, categorize, and count blood cells. Improved accuracy in blood cell detection and segmentation is the primary goal of this effort, which aims to identify three key groups of blood cells.
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