Blob Detection and Deep Learning for Leukemic Blood Image Analysis
In microscopy, laboratory tests make use of cell counters or flow cytometers to perform tests on blood cells, like the complete blood count, rapidly. However, a manual blood smear examination is still needed to verify the counter results and to monitor patients under therapy. Moreover, the manual in...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-02-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/10/3/1176 |
| _version_ | 1818278978859827200 |
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| author | Cecilia Di Ruberto Andrea Loddo Giovanni Puglisi |
| author_facet | Cecilia Di Ruberto Andrea Loddo Giovanni Puglisi |
| author_sort | Cecilia Di Ruberto |
| collection | DOAJ |
| description | In microscopy, laboratory tests make use of cell counters or flow cytometers to perform tests on blood cells, like the complete blood count, rapidly. However, a manual blood smear examination is still needed to verify the counter results and to monitor patients under therapy. Moreover, the manual inspection permits the description of the cells’ appearance, as well as any abnormalities. Unfortunately, manual analysis is long and tedious, and its result can be subjective and error-prone. Nevertheless, using image processing techniques, it is possible to automate the entire workflow, both reducing the operators’ workload and improving the diagnosis results. In this paper, we propose a novel method for recognizing white blood cells from microscopic blood images and classify them as healthy or affected by leukemia. The presented system is tested on public datasets for leukemia detection, the SMC-IDB, the IUMS-IDB, and the ALL-IDB. The results are promising, achieving 100% accuracy for the first two datasets and 99.7% for the ALL-IDB in white cells detection and 94.1% in leukemia classification, outperforming the state-of-the-art. |
| first_indexed | 2024-12-12T23:26:02Z |
| format | Article |
| id | doaj.art-ab7feea648f24dacbb4278c3296be1b0 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-12-12T23:26:02Z |
| publishDate | 2020-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-ab7feea648f24dacbb4278c3296be1b02022-12-22T00:08:05ZengMDPI AGApplied Sciences2076-34172020-02-01103117610.3390/app10031176app10031176Blob Detection and Deep Learning for Leukemic Blood Image AnalysisCecilia Di Ruberto0Andrea Loddo1Giovanni Puglisi2Department of Mathematics and Computer Science, University of Cagliari, 09124 Cagliari CA, ItalyDepartment of Mathematics and Computer Science, University of Cagliari, 09124 Cagliari CA, ItalyDepartment of Mathematics and Computer Science, University of Cagliari, 09124 Cagliari CA, ItalyIn microscopy, laboratory tests make use of cell counters or flow cytometers to perform tests on blood cells, like the complete blood count, rapidly. However, a manual blood smear examination is still needed to verify the counter results and to monitor patients under therapy. Moreover, the manual inspection permits the description of the cells’ appearance, as well as any abnormalities. Unfortunately, manual analysis is long and tedious, and its result can be subjective and error-prone. Nevertheless, using image processing techniques, it is possible to automate the entire workflow, both reducing the operators’ workload and improving the diagnosis results. In this paper, we propose a novel method for recognizing white blood cells from microscopic blood images and classify them as healthy or affected by leukemia. The presented system is tested on public datasets for leukemia detection, the SMC-IDB, the IUMS-IDB, and the ALL-IDB. The results are promising, achieving 100% accuracy for the first two datasets and 99.7% for the ALL-IDB in white cells detection and 94.1% in leukemia classification, outperforming the state-of-the-art.https://www.mdpi.com/2076-3417/10/3/1176leukocyte detectionleukemia classificationblob detectiondeep learning |
| spellingShingle | Cecilia Di Ruberto Andrea Loddo Giovanni Puglisi Blob Detection and Deep Learning for Leukemic Blood Image Analysis Applied Sciences leukocyte detection leukemia classification blob detection deep learning |
| title | Blob Detection and Deep Learning for Leukemic Blood Image Analysis |
| title_full | Blob Detection and Deep Learning for Leukemic Blood Image Analysis |
| title_fullStr | Blob Detection and Deep Learning for Leukemic Blood Image Analysis |
| title_full_unstemmed | Blob Detection and Deep Learning for Leukemic Blood Image Analysis |
| title_short | Blob Detection and Deep Learning for Leukemic Blood Image Analysis |
| title_sort | blob detection and deep learning for leukemic blood image analysis |
| topic | leukocyte detection leukemia classification blob detection deep learning |
| url | https://www.mdpi.com/2076-3417/10/3/1176 |
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