A Systematic Review on Recent Advancements in Deep and Machine Learning Based Detection and Classification of Acute Lymphoblastic Leukemia

Abstract

Automatic Leukemia or blood cancer detection is a challenging job and is very much required in healthcare centers. It has a significant role in early diagnosis and treatment planning. Leukemia is a hematological disorder that starts from the bone marrow and affects white blood cells (WBCs). Microscopic analysis of WBCs is a preferred approach for an early detection of Leukemia since it is cost-effective and less painful. Very few literature reviews have been done to demonstrate a comprehensive analysis of deep and machine learning-based Acute Lymphoblastic Leukemia (ALL) detection. This article presents a systematic review of the recent advancements in this knowledge domain. Here, various artificial intelligence-based ALL detection approaches are analyzed in a systematic manner with merits and demits. The review of these schemes is conducted in a structured manner. For this purpose, segmentation schemes are broadly categorized into signal and image processing-based techniques, conventional machine learning-based techniques, and deep learning-based techniques. Conventional machine learning-based ALL classification approaches are categorized into supervised and unsupervised machine learning is presented. In addition, deep learning-based classification methods are categorized into Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and the Autoencoder. Then, CNN-based classification schemes are further categorized into conventional CNN, transfer learning, and other advancements in CNN. A brief discussion of these schemes and their importance in ALL classification are also presented. Moreover, a critical analysis is performed to present a clear idea about the recent research in this field. Finally, various challenging issues and future scopes are discussed that may assist readers in formulating new research problems in this domain.