Early-stage leukaemia detection using sophisticated machine learning algorithms

Abstract

This paper investigates the efficacy of convolutional neural networks (CNNs), a deep learning technique, in early-stage leukaemia detection - a crucial task for improving outcomes. Comparing support vector machines, random forests, artificial neural networks, and CNNs, we assess performance on a dataset of blood samples from leukaemia patients and healthy subjects. Results reveal high accuracy across models, with CNN outperforming other methods in both accuracy and efficiency. CNNs capacity to learn complex patterns from raw data, such as blood samples, sets it apart from traditional algorithms. This study underscores CNNs potential to revolutionise early-stage leukaemia detection, demonstrating its significance in advancing cancer diagnosis.