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CBC and DBC Counter Using Image Processing and Analysis

Abstract:

In this study, the researchers developed a cost-effective way of performing Complete Blood Count (CBC) and Differential Blood Count (DBC) by implementing image processing, analysis, and machine learning algorithms using a Raspberry pi B+ microcontroller in conjunction with a microscope that has Short Message Service (SMS) feature to be used in rural areas for remote transmission of results to a health professional. Blood image input from the raspberry pi's camera will be used for the counting and classification needed by the system. The capturing and analysis of the images will be integrated on a graphical user interface (GUI) to provide user-friendly access. HSV color space conversion, thresholding, morphological operations, and Connected Component Labeling (CCL) are used for CBC. For DBC, an additional process of Convolutional Neural Network (CNN) classification is done. The study shows a remarkable result by having no significant difference from conventional method which is manual counting.