Microcell‐Net: A deep neural network for multi‐class classification of microscopic blood cell images

Abstract

AbstractBlood‐related diseases are one of the major concerns in the biomedical domain and most of the disease symptoms are reflected through the analysis of blood cells. The diagnosis by experts in laboratories is very costly and time‐consuming, thus artificial intelligence‐based systems can help in the automatic diagnosis and monitoring of an individual's health. In this study, a CNN‐based architecture Microcell‐Net is proposed which is trained on a microscopic image dataset of peripheral blood cells in eight different classes. The images have several inter‐class and intra‐class diversity with different magnification levels and the noise present in the images makes the classification task significantly challenging. Experimental results indicated that the proposed model can efficiently classify various types of microscopic blood cells with good accuracy. The experimental findings accomplished 98.76% validation accuracy and 97.65% test accuracy in complex background conditions. The performance of the model is compared with other state‐of‐the‐art models and the proposed deep neural network performs significantly better than others. The proposed model can be utilized in a real‐time diagnosis system because it is fast, automatic and efficient, which can assist in taking clinical decisions and early diagnosis of haematological disorders.