Leukemia Detection using Machine Learning Algorithms: Current trends and future directions

Amal A. Maryoosh

Mustansiriyah University

Saeid Pashazadeh

Keywords: Leukemia, Deep learning, Machine learning, Classification, Detection


Abstract

The process of medical diagnosis of leukemia cases is a complex process and requires diligent efforts. It is done by examining samples under a microscope and distinguishing the number, shape, size, and morphological features of white blood cells. This process takes a long time to predict leukemia. The professional skills and experience of the pathologist may also influence this procedure as a large number of overlapping structures and conditions, distractions, fatigue, and limitations in the human visual system can lead to an inappropriate diagnosis. Machine and deep learning approaches have been found to be effective strategies for enhancing the precision and efficiency of diagnosis and classification of medical images, including those of microscopic blood cells. In this paper, we will review the recent studies of leukemia detection and/or classification in the period of (2015 - 2023) in machine and deep learning and the combining of them. The review of these schemes is carried out in a systematic manner. In order to achieve the desired objective, segmentation schemes can be classified into four main categories: supervised machine learning techniques, unsupervised machine learning techniques, deep learning approaches, and traditional image processing techniques. Classification approaches can be categorized into three main groups: classical machine learning, deep learning, and a combination of both. CNN-based categorization systems can be further classified into three categories: conventional CNN, transfer learning, and additional developments in CNN. This paper also provides a concise analysis of these strategies and their significance in the categorization of leukemia. Ultimately, a comprehensive examination is conducted to elucidate the current state of research in this particular domain.