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• W4285255714 endingPage "314" @default.
• W4285255714 startingPage "293" @default.
• W4285255714 abstract "In recent development of machine learning (ML)-based medical image analysis that have contributed to the prediction, planning, and early diagnostic process. Different chronic hermitic diseases like blood cancer/leukemia, AIDs, malaria, anemia and even COVID-19, all these are diagnoses via analyzing leucocytes or white blood cells (WBCs). Leucocytes analysis is the process of detection, localization, counting, analyzing WBCs, and it perform an active role in clinical hematology to assist health specialists in early stage disease diagnosing process. An automatic leucocytes analysis provide valuable diagnostics facts to doctors, via they can automatically detect, blood cancer, brain tumor and significantly improve the hematological, pathological activities. Manual Detection, counting and classification of WBCs is very slow, challenging and boring task due to having complex overlapping and morphological uneven structure. In this chapter, we provide a concise analysis of available ML techniques, to use these techniques for leucocytes analysis in microscopic images. The main aim of this chapter is to identify high performer and suitable ML algorithms for WBCs analysis using blood microscopic smear images. In the proposed review study, the recent and most relevant research papers are collected from IEEE, Science Direct, springer, and web of science (WoS) with the following keywords: ‘leucocytes detection’ or ‘leucocytes classification’. This study gives an extensive review of MIA but the research focuses more on the ML-based leucocytes/WBCs analysis in smear images. These techniques include traditional machine learning (TML), deep learning (DL), convolutional neural network (CNN) models, hybrid learning, and attention learning-based techniques to analyze medical image modalities to detect and classify cells in smear images." @default.
• W4285255714 created "2022-07-14" @default.
• W4285255714 creator A5006616862 @default.
• W4285255714 creator A5011703110 @default.
• W4285255714 creator A5048466370 @default.
• W4285255714 creator A5052654998 @default.
• W4285255714 date "2022-01-01" @default.
• W4285255714 modified "2023-10-16" @default.
• W4285255714 title "A Review on Machine Learning-Based WBCs Analysis in Blood Smear Images: Key Challenges, Datasets, and Future Directions" @default.
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• W4285255714 doi "https://doi.org/10.1007/978-981-19-2057-8_11" @default.

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