Identification of Subtype Blood Cells Using Deep Learning Techniques

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Author(s): Parvathi R. (Vellore Institute of Technology, Chennai, India)and Pattabiraman V. (Vellore Institute of Technology, Chennai, India)
Copyright: 2022
Pages: 16
Source title: Handbook of Research on Technical, Privacy, and Security Challenges in a Modern World
Source Author(s)/Editor(s): Amit Kumar Tyagi (National Institute of Fashion Technology, New Delhi, India)
DOI: 10.4018/978-1-6684-5250-9.ch014

Keywords: Cyber and Network Security / Information Science Reference / Information Security & Privacy / Security & Forensics

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Abstract

The deep learning mechanism has indicated power in numerous applications and is recognized as a superior technique by an ever growing number of people than the conventional models of machine learning. In particular, the use of deep learning algorithms, particularly convolutional neural networks (CNN), brings immense benefits to the clinical sector, where an immense amount of images must be prepared and analyzed. A CNN-based framework is generated to automatically classify the images of blood cells into subtypes of cells. This chapter suggested the deep learning models, which are the convolutional neural network, the deep convolutional neural network, and a CNN-based model built in combination with the recurrent neural network (RNN), which is called RCNN, to identify the monocytes, lymphocytes, and types of WBCs. These are monocytes, eosinophils, lymphocytes, basophils, and neutrophils.

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Identification of Subtype Blood Cells Using Deep Learning Techniques

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