An efficient approach for Segmentation and Classification of WBC Nucleus from Microscopic blood smear

Abstract

With time, AI technologies have matured well and resonated in various domains of applied sciences and engineering. The sub-domains of AI, machine learning (ML), deep learning (DL), and associated statistical tools are getting more attention. Therefore, various machine learning models are being created to take advantage of the data available and accomplish tasks such as automatic prediction, classification, clustering, segmentation and anomaly detection, etc. Tasks like classification need labeled data used to train the models to achieve a reliable accuracy. According to the WHO annual report, the death rate related to blood diseases is very high in Asian continent. The existing traditional system is prolonged and tedious and also based on the expertise s knowledge. Therefore, the development of automated blood related disorder diagnostic system is very essential to make the system error free and more effective. As per the hematologist opinion, most of the disease can be identified by the White blood cells (WBC) related information. newlineThe white corpuscles nucleus segmentation from blood smear images is one of the major steps to diagnose blood-related diseases. The perfect and speedy segmentation system assists the hematologists identify the diseases and take appropriate decisions for better treatment. For this purpose, a huge amount of semi-automated systems is being developed using supervised or unsupervised learning. In this work, a hybrid Convolutional Neural Networks for classifying white corpuscle from blood smear images is presented. The major objective is to effectively classify various types of white corpuscle using CNNs, which will help with early disease identification and medical diagnosis. The suggested method entails gathering a varied dataset of blood smear images, standardizing them through preprocessing, and then using data augmentation approaches to improve model generalization. Medical images are processed using these DL methods to solve various tasks like prediction, segmentation, and classification with accuracy bypassing human abilities. However, the current DL models have some limitations that encourage the researchers to seek further improvement. newline