Abstract
A complete blood cell count is an important test in medical diagnosis to evaluate overall health condition. Traditionally blood cells are counted manually using haemocytometer along with other laboratory equipment's and chemical compounds, which is a time-consuming and tedious task. In this work, the authors present a machine learning approach for automatic identification and counting of three types of blood cells using ‘you only look once’ (YOLO) object detection and classification algorithm. YOLO framework has been trained with a modified configuration BCCD Dataset of blood smear images to automatically identify and count red blood cells, white blood cells, and platelets. Moreover, this study with other convolutional neural network architectures considering architecture complexity, reported accuracy, and running time with this framework and compare the accuracy of the models for blood cells detection. They also tested the trained model on smear images from a different dataset and found that the learned models are generalised. Overall the computer-aided system of detection and counting enables us to count blood cells from smear images in less than a second, which is useful for practical applications.
Highlights
A complete blood cell (CBC) count is an important test often requested by medical professionals to evaluate health condition [1, 2]
As these blood cells are huge in number, traditional manual blood cell counting system using haemocytometer is highly time consuming and erroneous and most of the cases accuracy vastly depends on the skills of a clinical laboratory analyst [3, 4]
It is seen from the table that, the highest accuracy for counting red blood cells (RBCs) and platelets is found for the Tiny you only look once (YOLO) architecture, which achieved 96.09% accuracy in counting RBCs and 96.36% accuracy in counting platelets
Summary
A complete blood cell (CBC) count is an important test often requested by medical professionals to evaluate health condition [1, 2]. RBCs known as erythrocytes are the most common type of blood cell, which consists of 40–45% of blood cells [American Society of Haematology: http://www.hematology.org/Patients/Basics/]. Platelets known as thrombocytes are in huge number in blood. WBCs known as leukocytes, are just 1% of total blood cells. RBCs carry oxygen to our body tissues and the amount of oxygen tissues receives is affected by the number of RBCs. WBCs fight against infections and platelets help with blood clotting. WBCs fight against infections and platelets help with blood clotting As these blood cells are huge in number, traditional manual blood cell counting system using haemocytometer is highly time consuming and erroneous and most of the cases accuracy vastly depends on the skills of a clinical laboratory analyst [3, 4]. An automated process to count different blood cells from a smear image will greatly facilitate the entire counting process
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