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A Survey on Automated Leukemia Detection and Classification: Exploring Image Processing and Gene Expression Analysis Approaches

by Priyush Panwar, D.A. Mehta
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 185 - Number 23
Year of Publication: 2023
Authors: Priyush Panwar, D.A. Mehta
10.5120/ijca2023922978

Priyush Panwar, D.A. Mehta . A Survey on Automated Leukemia Detection and Classification: Exploring Image Processing and Gene Expression Analysis Approaches. International Journal of Computer Applications. 185, 23 ( Jul 2023), 1-8. DOI=10.5120/ijca2023922978

@article{ 10.5120/ijca2023922978,
author = { Priyush Panwar, D.A. Mehta },
title = { A Survey on Automated Leukemia Detection and Classification: Exploring Image Processing and Gene Expression Analysis Approaches },
journal = { International Journal of Computer Applications },
issue_date = { Jul 2023 },
volume = { 185 },
number = { 23 },
month = { Jul },
year = { 2023 },
issn = { 0975-8887 },
pages = { 1-8 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume185/number23/32829-2023922978/ },
doi = { 10.5120/ijca2023922978 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:26:49.588029+05:30
%A Priyush Panwar
%A D.A. Mehta
%T A Survey on Automated Leukemia Detection and Classification: Exploring Image Processing and Gene Expression Analysis Approaches
%J International Journal of Computer Applications
%@ 0975-8887
%V 185
%N 23
%P 1-8
%D 2023
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Leukemia is a haematological disorder which affects blood and bone marrow. Automated leukemia detection is significant for facilitating timely diagnoses and increasing the chances of successful treatment outcomes. Further, the classification of leukemia is crucial because each type demands a distinct treatment strategy. Deep learning and machine learning techniques are being used by computer aided diagnosis systems for their high precision in identifying leukemia from microscopic blood smear images using image processing techniques and gene microarray data using gene expression analysis techniques. However, automation of this task using image processing techniques is challenging due to the uneven structure and overlapping of cells. The large number of genes in microarray data makes the classification challenging, which is accomplished by applying a variety of feature selection techniques. In this study, recent developments in automated leukemia detection and classification employing image processing and gene expression analysis methods are explored. Furthermore, the paper compares and contrasts various techniques to provide a comprehensive overview that can assist in the continuous refinement of the detection and classification process.

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Index Terms

Computer Science
Information Sciences

Keywords

Leukemia Image Segmentation Convolutional Neural Networks Feature Selection

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