A Review on Traditional Machine Learning and Deep Learning Models for WBCs Classification in Blood Smear Images

In computer vision, traditional machine learning (TML) and deep learning (DL) methods have significantly contributed to the advancements of medical image analysis (MIA) by enhancing prediction accuracy, leading to appropriate planning and diagnosis. These methods substantially improved the diagnoses...

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Bibliographic Details
Published in:IEEE access 2021, Vol.9, p.10657-10673
Main Authors: Khan, Siraj, Sajjad, Muhammad, Hussain, Tanveer, Ullah, Amin, Imran, Ali Shariq
Format: Article
Language:English
Subjects:
Artificial neural networks
Blood
Blood smear images
Cancer
CNN
Computed tomography
Computer vision
Deep learning
Diseases
Domains
Feature extraction
Image analysis
Image classification
Image enhancement
Leukemia
Leukocytes
Literature reviews
Machine learning
Magnetic resonance imaging
Medical diagnostic imaging
medical image analysis
Medical imaging
Medical services
Microscopy
Physicians
Solid modeling
traditional machine learning
WBCs classification
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Summary:In computer vision, traditional machine learning (TML) and deep learning (DL) methods have significantly contributed to the advancements of medical image analysis (MIA) by enhancing prediction accuracy, leading to appropriate planning and diagnosis. These methods substantially improved the diagnoses of automatic brain tumor and leukemia/blood cancer detection and can assist the hematologist and doctors by providing a second opinion. This review provides an in-depth analysis of available TML and DL techniques for MIA with a significant focus on leukocytes classification in blood smear images and other medical imaging domains, i.e., magnetic resonance imaging (MRI), CT images, X-ray, and ultrasounds. The proposed review's main impact is to find the most suitable TML and DL techniques in MIA, especially for leukocyte classification in blood smear images. The advanced DL techniques, particularly the evolving convolutional neural networks-based models in the MIA domain, are deeply investigated in this review article. The related literature study reveals that mainstream TML methods are vastly applied to microscopic blood smear images for white blood cells (WBC) analysis. They provide valuable information to medical specialists and help diagnose various hematic diseases such as AIDS and blood cancer (Leukaemia). Based on WBC related literature study and its extensive analysis presented in this study, we derive future research directions for scientists and practitioners working in the MIA domain.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2020.3048172