• Shreyas Raju A
• Dr. Muzameel Ahmed

The analysis of blood smear is an important medical diagnostic test to evaluate the general state of health. Generally blood cells are counted manually by means of a hemocytometer alongside other laboratory equipment’s and chemicals, this process requires a lot of time and effort. In this work a method is proposed using Machine learning technique for analysing the blood smear for classification of blood cell types like Erythrocytes known as Red Blood Cells (RBC), Leukocytes known as White Blood Cells (WBC), and Thrombocytes known as Platelets using “YOLO - You Only Look Once” state of the art object detection and classification algorithm. The YOLO network is trained with dataset collected from Blood Cell Count Dataset (BCCD) of blood smear images. The trained YOLO frame work will identify and classify the blood cells into respective blood cell category. The result obtained here has better accuracy and performance, compared with other CNN - Convolutional neural network architecture.

Platelets, White Blood Cells, Red Blood Cells, You Only Look Once, Blood Cell Count Dataset, Convolutional Neural Network.

"Analysing Microscopic Images Of Peripheral Blood Smear Images Using Deep Learning ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 8, page no.a441-a447, August-2022, Available :http://www.jetir.org/papers/JETIR2208058.pdf

"Analysing Microscopic Images Of Peripheral Blood Smear Images Using Deep Learning ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 8, page no. ppa441-a447, August-2022, Available at : http://www.jetir.org/papers/JETIR2208058.pdf