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Deep-CNN Model for Acute Lymphocytic Leukemia (ALL) Classification Using Microscopic Blood Images: Global Research

Deep-CNN Model for Acute Lymphocytic Leukemia (ALL) Classification Using Microscopic Blood Images: Global Research
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Author(s): Prasanna Ranjith Christodoss (University of Technology and Applied Sciences, Shinas, Oman) and Rajesh Natarajan (University of Applied Science and Technology, Shinas, Oman)
Copyright: 2022
Pages: 14
Source title: Handbook of Research on Technologies and Systems for E-Collaboration During Global Crises
Source Author(s)/Editor(s): Jingyuan Zhao (University of Toronto, Canada) and V. Vinoth Kumar (Jain University, India)
DOI: 10.4018/978-1-7998-9640-1.ch001



Acute lymphocytic leukemia (ALL) is a variety of malignant somatic cell cancer that influences children and teenagers. The goal of the study is to create a system that can detect cancer from blood corpuscle images mechanically. This method employs a convolutional network that takes images of blood corpuscles and determines whether or not the cell is cancer infected. The appearance of cancer in blood corpuscle images is frequently ambiguous, overlaps with other diagnosis, and can be mistaken for a variety of benign abnormalities. Machine-assisted cancer identification from blood corpuscle images at the level of skilled medical staff would be extremely beneficial in clinical settings and also in the delivery of healthcare to populations with limited access to diagnostic imaging specialists. Here, the authors proposed a convolutional neural network (CNN)-based methodology to distinguish between outdated as well as irregular somatic cell photos. With the dataset and 1188 somatic cell images, the proposed methodology achieves an accuracy of up to 96.6%.

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