Blood Cell Microscopic Image Classification in Computer Aided Diagnosis Using Machine Learning: A Review

Abstract

Blood cell detection considers a gold standard key in diagnosing blood disease and producing automatic reports to hematologists and doctors. Blood cell detection is a challenging task due to non-illumination level, high number of overlapped cells per image, variations in cell densities among platelets, white blood cells and red blood cells, and the variety of staining process. Traditional procedure of blood cell detection requires pathologist effort and time. In computer aided diagnosis, machine learning and deep learning techniques become the practical way to automate the procedure of diagnosing, classify microscopic blood cells, and increase the accuracy and speed of the procedure. This paper provides a review of the detection and classification of blood cell, including red blood cells, white blood cells and platelets and their characteristics using machine learning techniques. We also have detailed the dataset of microscope blood cell. We have divided the previous works into four categories based on the output of the models, including pre-processing, segmentation, feature extraction and classification. Then, we discuss the challenges that face these methods and suggest the potential future techniques.

Blood cell image segmentation and classification: a systematic review

“…This issue is consistently highlighted as a significant challenge in developing and validating new models. Another common challenge mentioned in the surveys ( Das et al, 2022 ; Khan et al, 2021 ; Anilkumar, Manoj & Sagi, 2023 ; Raina et al, 2023 ; Rao & Rao, 2023 ; Umamaheswari & Geetha, 2019 ; More & Sugandhi, 2023 ; Al-Dulaimi & Makki, 2023 ; Veeraiah, Alotaibi & Subahi, 2023 ) is the inherent complexity in microscopic images. This shows a demanding need for robust algorithms that can handle such complexities.…”

“…The survey in Veeraiah, Alotaibi & Subahi (2023) focuses on a technical article that proposes a new method, the Histogram Threshold Segmentation Classifier (HTsC), rather than a traditional survey. Regarding weaknesses, some of the previous works have some common limitations, including ( Khan et al, 2021 ; Thomas & Sreejith, 2018 ; Byndur et al, 2023 ; Rao & Rao, 2023 ; Umamaheswari & Geetha, 2019 ; Al-Dulaimi & Makki, 2023 ; Asghar et al, 2023 ), the absence of experimental results or comprehensive comparisons between methods. In contrast, the proposed survey appears to be more comprehensive and holistic.…”