Quantitative analysis of blood cells from microscopic images using Convolutional Neural Network
Abel Worku, Timothy Kwa, Mohammed Aliy Mohammed, Gelan Ayana Zewdie, Gizeaddis Lamesgin Simegn · 2019
Blood cell count provides relevant clinical information about different kinds of disorders. Any deviation in the number of blood cells implies the presence of infection, inflammation, edema, bleeding, and other blood-related issues. Current microscopic methods used for blood cell counting are very tedious and are highly prone to different sources of errors. In addition, these techniques do not provide full information related to blood cells like shape and size, which play important roles in the clinical investigation of serious blood-related diseases. In this paper, deep learning-based automatic classification and quantitative analysis of blood cells is proposed using the YOLOv2 model. The model was trained on 1,560 images and 2,703-labeled blood cells with different hyper-parameters. It was tested on 26 images containing 1,454 red blood cells, 159 Platelets, 3 Basophils, 12 Eosinophils, 24 Lymphocytes, 13 Monocytes, and 28 Neutrophils. The network achieved detection and segmentation of blood cells with an average accuracy of 80.6% and a precision of 88.4%. Quantitative analysis of cells was done following classification, and mean accuracy of 92.96%, 91.96%, 88.736%, and 92.7% has been achieved in the measurement of area, aspect ratio, diameter and counting of cells respectively.