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On the Effectiveness of Leukocytes Classification Methods in a Real Application Scenario

by 1,* and 2,*
1
Department of Mathematics and Computer Science, University of Cagliari, via Ospedale 72, 09124 Cagliari, Italy
2
Department of Electrical and Electronic Engineering, University of Cagliari, Piazza d’Armi, 09123 Cagliari, Italy
*
Authors to whom correspondence should be addressed.
Academic Editor: Yiannis Ampatzidis
AI 2021, 2(3), 394-412; https://doi.org/10.3390/ai2030025
Received: 15 June 2021 / Revised: 20 July 2021 / Accepted: 23 August 2021 / Published: 25 August 2021
(This article belongs to the Topic Medical Image Processing)
Automating the analysis of digital microscopic images to identify the cell sub-types or the presence of illness has assumed a great importance since it aids the laborious manual process of review and diagnosis. In this paper, we have focused on the analysis of white blood cells. They are the body’s main defence against infections and diseases and, therefore, their reliable classification is very important. Current systems for leukocyte analysis are mainly dedicated to: counting, sub-types classification, disease detection or classification. Although these tasks seem very different, they share many steps in the analysis process, especially those dedicated to the detection of cells in blood smears. A very accurate detection step gives accurate results in the classification of white blood cells. Conversely, when detection is not accurate, it can adversely affect classification performance. However, it is very common in real-world applications that work on inaccurate or non-accurate regions. Many problems can affect detection results. They can be related to the quality of the blood smear images, e.g., colour and lighting conditions, absence of standards, or even density and presence of overlapping cells. To this end, we performed an in-depth investigation of the above scenario, simulating the regions produced by detection-based systems. We exploit various image descriptors combined with different classifiers, including CNNs, in order to evaluate which is the most suitable in such a scenario, when performing two different tasks: Classification of WBC subtypes and Leukaemia detection. Experimental results have shown that Convolutional Neural Networks are very robust in such a scenario, outperforming common machine learning techniques combined with hand-crafted descriptors. However, when exploiting appropriate images for model training, even simpler approaches can lead to accurate results in both tasks. View Full-Text
Keywords: white blood cells analysis; cell sub-types; leukaemia detection; feature extraction; classification white blood cells analysis; cell sub-types; leukaemia detection; feature extraction; classification
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MDPI and ACS Style

Loddo, A.; Putzu, L. On the Effectiveness of Leukocytes Classification Methods in a Real Application Scenario. AI 2021, 2, 394-412. https://doi.org/10.3390/ai2030025

AMA Style

Loddo A, Putzu L. On the Effectiveness of Leukocytes Classification Methods in a Real Application Scenario. AI. 2021; 2(3):394-412. https://doi.org/10.3390/ai2030025

Chicago/Turabian Style

Loddo, Andrea, and Lorenzo Putzu. 2021. "On the Effectiveness of Leukocytes Classification Methods in a Real Application Scenario" AI 2, no. 3: 394-412. https://doi.org/10.3390/ai2030025

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