Acute leukemia prediction and classification using convolutional neural network and generative adversarial network

Abstract

Acute Leukemia (AL) is a type of cancer that affects the blood cells and can be fatal if not detected and treated early. Flow cytometry is a key means of detecting AL, but requires manual processing by trained professionals, applying the manual gating method to huge amounts of raw data, making it time-consuming and labor-intensive. This study uses the deep learning Convolutional Neural Network (CNN) method for classification prediction based on two-dimensional graph for the parameter combinations of flow cytometry data to differentiate AL as normal, Acute Myeloid Leukemia (AML) and Acute Lymphoblastic Leukemia (ALL) three categories. This approach normalizes raw data, removes outliers, and further augments data using Generative Adversarial Networks (GAN) to generate two-dimensional scattergrams of different parameter combinations generated by four methods. We then assess post-training classification prediction accuracy to identify the most effective preprocessing method and parameter combination. Experimental results obtain accuracy rates of 73–86 %. In the two-dimensional scatter plot of different parameter combinations generated by the four methods, the combination of CD3 and CD7 cell population parameters had the best classification and prediction accuracy, with image classification and identification reaching a high of 86 % through the use of GAN-based data augmentation. Advances in medical testing technology has reduced analysis times and increased data throughput. At the same time, artificial intelligence techniques are increasingly used for analysis and detection, performing large numbers of repetitive actions to reduce the potential for subjective assessment error from manual analysis, thus allowing for the earlier detection and treatment of disease.