Deep Learning Neural Network Based on PSO for Leukemia Cell Disease Diagnosis from Microscope Images

Abstract

Leukemia is a kind of cancer characterized by the proliferation of abnormal, immature White Blood Cells (WBCs) produced in the bone marrow, which subsequently circulate throughout the body. Prompt leukemia diagnosis is vital in determining the optimal treatment plan, as different types of leukemia require distinct treatments. Early detection is therefore instrumental in facilitating the use of the most effective therapies. The identification of leukemia cells from microscopic images is considered a challenging task due to the complexity of the image features. This paper presents a deep learning neural network approach that utilizes the Particle Swarm Optimization (PSO) method to diagnose leukemia cell disease from microscope images. Initially, deep learning is employed to extract features from the leukemia images, which are then optimized by the PSO method to select the most relevant features for machine learning. Three different machine learning algorithms, namely Decision Tree (DT), Support Vector Machine (SVM), and K-Nearest Neighbors (K-NN) methods, are utilized to analyze the selected features. The results of the experiments demonstrate PSO accuracies of 97.4%, 92.3%, and 85.9% for SVM, K-NN, and DT algorithms with GoogLeNet, respectively. The proposed method achieved accuracies of 100%, 94.9%, and 92.3% for SVM, K-NN, and DT methods respectively, with Ant Colony Optimization (ACO) feature extraction and ResNet-50 employed as revealed by the experimental results. These findings suggest that the proposed approach is a promising tool for accurate diagnosis of leukemia cell disease using microscopic images.