STM Journals Peer-reviewed research from Consortium e-Learning Network

International Journal of Genetic Modifications and Recombinations Review Article

A Systematic Review on Leukemia Detection and Classification Techniques Using Gene Expression

  1. Veerpal Kaur Department of Computer Science and Engineering Punjabi University Patiala
  2. Dr. Jasvir Singh Department of Computer Science and Engineering Punjabi University Patiala
  3. Dr. Neelofar Sohi Department of Computer Science and Engineering Punjabi University Patiala

Abstract

Early diagnosis of genetic diseases is crucial for effective treatment, especially in the case of Leukemia, a type of blood cancer characterized by abnormal proliferation of white blood cells. This paper presents a systematic review of recent computational techniques for the detection and classification of Leukemia using gene expression data obtained from DNA microarray analysis. The study explores diverse methodologies including machine learning (ML), deep learning (DL), and bio-inspired algorithms for identifying Leukemia subtypes such as AML (Acute Myeloid Leukemia) and ALL (Acute Lymphoblastic Leukemia). Several models such as Support Vector Machines, k-Nearest Neighbors, Artificial Neural Networks, Deep Neural Networks, and ensemble learning methods have been discussed for their accuracy and effectiveness in handling high-dimensional microarray datasets. Moreover, feature selection techniques like Genetic Algorithms, Particle Swarm Optimization, and recent hybrid models such as ACO-ALO and SCBAO are reviewed for enhancing classification accuracy. Recent advancements also incorporate entropy-based and multi-class feature extraction to improve performance. While these approaches demonstrate high precision in classification, challenges such as limited datasets, computational cost, and clinical validation remain. The review highlights the potential of hybrid and integrative models for robust and scalable Leukemia diagnosis, emphasizing the need for continued research to bridge computational advancements with clinical applicability.

Keywords

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