Classification of white blood cells (leucocytes) from blood smear imagery using machine and deep learning models: A global scoping review.
Machine learning (ML) and deep learning (DL) models are being increasingly employed for medical imagery analyses, with both approaches used to enhance the accuracy of classification/prediction in the diagnoses of various cancers, tumors and bloodborne diseases. To date however, no review of these te...
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Public Library of Science (PLoS)
2024-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0292026 |
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| author | Rabia Asghar Sanjay Kumar Arslan Shaukat Paul Hynds |
| author_facet | Rabia Asghar Sanjay Kumar Arslan Shaukat Paul Hynds |
| author_sort | Rabia Asghar |
| collection | DOAJ |
| description | Machine learning (ML) and deep learning (DL) models are being increasingly employed for medical imagery analyses, with both approaches used to enhance the accuracy of classification/prediction in the diagnoses of various cancers, tumors and bloodborne diseases. To date however, no review of these techniques and their application(s) within the domain of white blood cell (WBC) classification in blood smear images has been undertaken, representing a notable knowledge gap with respect to model selection and comparison. Accordingly, the current study sought to comprehensively identify, explore and contrast ML and DL methods for classifying WBCs. Following development and implementation of a formalized review protocol, a cohort of 136 primary studies published between January 2006 and May 2023 were identified from the global literature, with the most widely used techniques and best-performing WBC classification methods subsequently ascertained. Studies derived from 26 countries, with highest numbers from high-income countries including the United States (n = 32) and The Netherlands (n = 26). While WBC classification was originally rooted in conventional ML, there has been a notable shift toward the use of DL, and particularly convolutional neural networks (CNN), with 54.4% of identified studies (n = 74) including the use of CNNs, and particularly in concurrence with larger datasets and bespoke features e.g., parallel data pre-processing, feature selection, and extraction. While some conventional ML models achieved up to 99% accuracy, accuracy was shown to decrease in concurrence with decreasing dataset size. Deep learning models exhibited improved performance for more extensive datasets and exhibited higher levels of accuracy in concurrence with increasingly large datasets. Availability of appropriate datasets remains a primary challenge, potentially resolvable using data augmentation techniques. Moreover, medical training of computer science researchers is recommended to improve current understanding of leucocyte structure and subsequent selection of appropriate classification models. Likewise, it is critical that future health professionals be made aware of the power, efficacy, precision and applicability of computer science, soft computing and artificial intelligence contributions to medicine, and particularly in areas like medical imaging. |
| format | Article |
| id | doaj-art-9a1d0969d8e046c9b2abbf7f2fbb42ed |
| institution | Kabale University |
| issn | 1932-6203 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Public Library of Science (PLoS) |
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| series | PLoS ONE |
| spelling | doaj-art-9a1d0969d8e046c9b2abbf7f2fbb42ed2025-02-05T05:32:26ZengPublic Library of Science (PLoS)PLoS ONE1932-62032024-01-01196e029202610.1371/journal.pone.0292026Classification of white blood cells (leucocytes) from blood smear imagery using machine and deep learning models: A global scoping review.Rabia AsgharSanjay KumarArslan ShaukatPaul HyndsMachine learning (ML) and deep learning (DL) models are being increasingly employed for medical imagery analyses, with both approaches used to enhance the accuracy of classification/prediction in the diagnoses of various cancers, tumors and bloodborne diseases. To date however, no review of these techniques and their application(s) within the domain of white blood cell (WBC) classification in blood smear images has been undertaken, representing a notable knowledge gap with respect to model selection and comparison. Accordingly, the current study sought to comprehensively identify, explore and contrast ML and DL methods for classifying WBCs. Following development and implementation of a formalized review protocol, a cohort of 136 primary studies published between January 2006 and May 2023 were identified from the global literature, with the most widely used techniques and best-performing WBC classification methods subsequently ascertained. Studies derived from 26 countries, with highest numbers from high-income countries including the United States (n = 32) and The Netherlands (n = 26). While WBC classification was originally rooted in conventional ML, there has been a notable shift toward the use of DL, and particularly convolutional neural networks (CNN), with 54.4% of identified studies (n = 74) including the use of CNNs, and particularly in concurrence with larger datasets and bespoke features e.g., parallel data pre-processing, feature selection, and extraction. While some conventional ML models achieved up to 99% accuracy, accuracy was shown to decrease in concurrence with decreasing dataset size. Deep learning models exhibited improved performance for more extensive datasets and exhibited higher levels of accuracy in concurrence with increasingly large datasets. Availability of appropriate datasets remains a primary challenge, potentially resolvable using data augmentation techniques. Moreover, medical training of computer science researchers is recommended to improve current understanding of leucocyte structure and subsequent selection of appropriate classification models. Likewise, it is critical that future health professionals be made aware of the power, efficacy, precision and applicability of computer science, soft computing and artificial intelligence contributions to medicine, and particularly in areas like medical imaging.https://doi.org/10.1371/journal.pone.0292026 |
| spellingShingle | Rabia Asghar Sanjay Kumar Arslan Shaukat Paul Hynds Classification of white blood cells (leucocytes) from blood smear imagery using machine and deep learning models: A global scoping review. PLoS ONE |
| title | Classification of white blood cells (leucocytes) from blood smear imagery using machine and deep learning models: A global scoping review. |
| title_full | Classification of white blood cells (leucocytes) from blood smear imagery using machine and deep learning models: A global scoping review. |
| title_fullStr | Classification of white blood cells (leucocytes) from blood smear imagery using machine and deep learning models: A global scoping review. |
| title_full_unstemmed | Classification of white blood cells (leucocytes) from blood smear imagery using machine and deep learning models: A global scoping review. |
| title_short | Classification of white blood cells (leucocytes) from blood smear imagery using machine and deep learning models: A global scoping review. |
| title_sort | classification of white blood cells leucocytes from blood smear imagery using machine and deep learning models a global scoping review |
| url | https://doi.org/10.1371/journal.pone.0292026 |
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