WBC image classification and generative models based on convolutional neural network
Abstract Background Computer-aided methods for analyzing white blood cells (WBC) are popular due to the complexity of the manual alternatives. Recent works have shown highly accurate segmentation and detection of white blood cells from microscopic blood images. However, the classification of the obs...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2022-05-01
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| Series: | BMC Medical Imaging |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12880-022-00818-1 |
| _version_ | 1811341280441008128 |
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| author | Changhun Jung Mohammed Abuhamad David Mohaisen Kyungja Han DaeHun Nyang |
| author_facet | Changhun Jung Mohammed Abuhamad David Mohaisen Kyungja Han DaeHun Nyang |
| author_sort | Changhun Jung |
| collection | DOAJ |
| description | Abstract Background Computer-aided methods for analyzing white blood cells (WBC) are popular due to the complexity of the manual alternatives. Recent works have shown highly accurate segmentation and detection of white blood cells from microscopic blood images. However, the classification of the observed cells is still a challenge, in part due to the distribution of the five types that affect the condition of the immune system. Methods (i) This work proposes W-Net, a CNN-based method for WBC classification. We evaluate W-Net on a real-world large-scale dataset that includes 6562 real images of the five WBC types. (ii) For further benefits, we generate synthetic WBC images using Generative Adversarial Network to be used for education and research purposes through sharing. Results (i) W-Net achieves an average accuracy of 97%. In comparison to state-of-the-art methods in the field of WBC classification, we show that W-Net outperforms other CNN- and RNN-based model architectures. Moreover, we show the benefits of using pre-trained W-Net in a transfer learning context when fine-tuned to specific task or accommodating another dataset. (ii) The synthetic WBC images are confirmed by experiments and a domain expert to have a high degree of similarity to the original images. The pre-trained W-Net and the generated WBC dataset are available for the community to facilitate reproducibility and follow up research work. Conclusion This work proposed W-Net, a CNN-based architecture with a small number of layers, to accurately classify the five WBC types. We evaluated W-Net on a real-world large-scale dataset and addressed several challenges such as the transfer learning property and the class imbalance. W-Net achieved an average classification accuracy of 97%. We synthesized a dataset of new WBC image samples using DCGAN, which we released to the public for education and research purposes. |
| first_indexed | 2024-04-13T18:53:42Z |
| format | Article |
| id | doaj.art-80278a286bc040a68afdc58a5a9e04cd |
| institution | Directory Open Access Journal |
| issn | 1471-2342 |
| language | English |
| last_indexed | 2024-04-13T18:53:42Z |
| publishDate | 2022-05-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Imaging |
| spelling | doaj.art-80278a286bc040a68afdc58a5a9e04cd2022-12-22T02:34:20ZengBMCBMC Medical Imaging1471-23422022-05-0122111610.1186/s12880-022-00818-1WBC image classification and generative models based on convolutional neural networkChanghun Jung0Mohammed Abuhamad1David Mohaisen2Kyungja Han3DaeHun Nyang4Department of Cyber Security, Ewha Womans UniversityDepartment of Computer Science, Loyola University ChicagoDepartment of Computer Science, University of Central FloridaDepartment of Laboratory Medicine and College of Medicine, The Catholic University of Korea Seoul St. Mary’s HospitalDepartment of Cyber Security, Ewha Womans UniversityAbstract Background Computer-aided methods for analyzing white blood cells (WBC) are popular due to the complexity of the manual alternatives. Recent works have shown highly accurate segmentation and detection of white blood cells from microscopic blood images. However, the classification of the observed cells is still a challenge, in part due to the distribution of the five types that affect the condition of the immune system. Methods (i) This work proposes W-Net, a CNN-based method for WBC classification. We evaluate W-Net on a real-world large-scale dataset that includes 6562 real images of the five WBC types. (ii) For further benefits, we generate synthetic WBC images using Generative Adversarial Network to be used for education and research purposes through sharing. Results (i) W-Net achieves an average accuracy of 97%. In comparison to state-of-the-art methods in the field of WBC classification, we show that W-Net outperforms other CNN- and RNN-based model architectures. Moreover, we show the benefits of using pre-trained W-Net in a transfer learning context when fine-tuned to specific task or accommodating another dataset. (ii) The synthetic WBC images are confirmed by experiments and a domain expert to have a high degree of similarity to the original images. The pre-trained W-Net and the generated WBC dataset are available for the community to facilitate reproducibility and follow up research work. Conclusion This work proposed W-Net, a CNN-based architecture with a small number of layers, to accurately classify the five WBC types. We evaluated W-Net on a real-world large-scale dataset and addressed several challenges such as the transfer learning property and the class imbalance. W-Net achieved an average classification accuracy of 97%. We synthesized a dataset of new WBC image samples using DCGAN, which we released to the public for education and research purposes.https://doi.org/10.1186/s12880-022-00818-1White blood cellClassificationMedical imageCNNDeep learning |
| spellingShingle | Changhun Jung Mohammed Abuhamad David Mohaisen Kyungja Han DaeHun Nyang WBC image classification and generative models based on convolutional neural network BMC Medical Imaging White blood cell Classification Medical image CNN Deep learning |
| title | WBC image classification and generative models based on convolutional neural network |
| title_full | WBC image classification and generative models based on convolutional neural network |
| title_fullStr | WBC image classification and generative models based on convolutional neural network |
| title_full_unstemmed | WBC image classification and generative models based on convolutional neural network |
| title_short | WBC image classification and generative models based on convolutional neural network |
| title_sort | wbc image classification and generative models based on convolutional neural network |
| topic | White blood cell Classification Medical image CNN Deep learning |
| url | https://doi.org/10.1186/s12880-022-00818-1 |
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