Deep learning for an automated image-based stem cell classification
Hematopoiesis is a process in which hematopoietic stem cells produce other mature blood cells in the bone marrow through cell proliferation and differentiation. The hematopoietic cells are cultured on a petri dish to form a different colony-forming unit (CFU). The idea is to identify the type of CFU...
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Penerbit Universiti Kebangsaan Malaysia
2023
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| Online Access: | http://journalarticle.ukm.my/22842/1/18%20%281%29.pdf http://journalarticle.ukm.my/22842/ https://www.ukm.my/jkukm/volume-3505-2023/ |
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| author | Nurul Syahira Mohamad Zamani, Hoe, Ernest Yoon Choong Aqilah Baseri Huddin, Wan Mimi Diyana Wan Zaki, Zariyantey Abd Hamid, |
| author_facet | Nurul Syahira Mohamad Zamani, Hoe, Ernest Yoon Choong Aqilah Baseri Huddin, Wan Mimi Diyana Wan Zaki, Zariyantey Abd Hamid, |
| author_sort | Nurul Syahira Mohamad Zamani, |
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| collection | Institutional Repository |
| content_provider | Universiti Kebangsaan Malaysia |
| content_source | UKM Journal Article Repository |
| continent | Asia |
| country | Malaysia |
| description | Hematopoiesis is a process in which hematopoietic stem cells produce other mature blood cells in the bone marrow through cell proliferation and differentiation. The hematopoietic cells are cultured on a petri dish to form a different colony-forming unit (CFU). The idea is to identify the type of CFU produced by the stem cell. Several software has been developed to classify the CFU automatically. However, an automated identification or classification of CFU types has become the main challenge. Most of the current software has common drawbacks, such as the expensive operating cost and complex machines. The purpose of this study is to investigate several selected convolutional neural network (CNN) pre-trained models to overcome these constraints for automated CFU classification. Prior to CFU classification, the images are acquired from mouse stem cells and categorized into three types which are CFU-erythroid (E), CFU-granulocyte/macrophage (GM) and CFU-PreB. These images are then pre-processed before being fed into CNN pre-trained models. The models adopt a deep learning neural network approach to extract informative features from the CFU images Classification performance shows that the models integrated with the pre-processing module can classify the CFUs with high accuracies and shorter computational time with 96.33% on 61 minutes and 37 seconds, respectively. Hence, this work finding could be used as the baseline reference for further research.Keywords: Automated stem cell classification; Colony-forming unit (CFU); Deep learning; Convolutional neural network (CNN)
Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi, MalaysiabOptometry and Vision Sciences Programme, Faculty of Health Sciences, School of Healthcare Sciences, Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia*proliferation and differentiation. The hematopoietic cells are cultured on a petri dish to form a different colony-forming unit (CFU). The idea is to identify the type of CFU produced by the stem cell. Several software has been developed to classify the CFU automatically. However, an automated identification or classification of CFU types has become the main challenge. Most of the current software has common drawbacks, such as the expensive operating cost and complex machines. The purpose of this study is to investigate several selected convolutional neural network (CNN) pre-trained models to overcome these constraints for automated CFU classification. Prior to CFU classification, the images are acquired from mouse stem cells and categorized into three types which are CFU-erythroid (E), CFU-granulocyte/macrophage (GM) and CFU-PreB. These images are then pre-processed before being fed into CNN pre-trained models. The models adopt a deep learning neural network approach to extract informative features from the CFU images Classification performance shows that the models integrated with the pre-processing module can classify the CFUs with high accuracies and shorter computational time with 96.33% on 61 minutes and 37 seconds, respectively. Hence, this work finding could be used as the baseline reference for further research. |
| format | Article |
| id | my-ukm.journal.22842 |
| institution | Universiti Kebangsaan Malaysia |
| language | en |
| publishDate | 2023 |
| publisher | Penerbit Universiti Kebangsaan Malaysia |
| record_format | eprints |
| spelling | my-ukm.journal.228422024-01-11T02:44:33Z http://journalarticle.ukm.my/22842/ Deep learning for an automated image-based stem cell classification Nurul Syahira Mohamad Zamani, Hoe, Ernest Yoon Choong Aqilah Baseri Huddin, Wan Mimi Diyana Wan Zaki, Zariyantey Abd Hamid, Hematopoiesis is a process in which hematopoietic stem cells produce other mature blood cells in the bone marrow through cell proliferation and differentiation. The hematopoietic cells are cultured on a petri dish to form a different colony-forming unit (CFU). The idea is to identify the type of CFU produced by the stem cell. Several software has been developed to classify the CFU automatically. However, an automated identification or classification of CFU types has become the main challenge. Most of the current software has common drawbacks, such as the expensive operating cost and complex machines. The purpose of this study is to investigate several selected convolutional neural network (CNN) pre-trained models to overcome these constraints for automated CFU classification. Prior to CFU classification, the images are acquired from mouse stem cells and categorized into three types which are CFU-erythroid (E), CFU-granulocyte/macrophage (GM) and CFU-PreB. These images are then pre-processed before being fed into CNN pre-trained models. The models adopt a deep learning neural network approach to extract informative features from the CFU images Classification performance shows that the models integrated with the pre-processing module can classify the CFUs with high accuracies and shorter computational time with 96.33% on 61 minutes and 37 seconds, respectively. Hence, this work finding could be used as the baseline reference for further research.Keywords: Automated stem cell classification; Colony-forming unit (CFU); Deep learning; Convolutional neural network (CNN) Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi, MalaysiabOptometry and Vision Sciences Programme, Faculty of Health Sciences, School of Healthcare Sciences, Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia*proliferation and differentiation. The hematopoietic cells are cultured on a petri dish to form a different colony-forming unit (CFU). The idea is to identify the type of CFU produced by the stem cell. Several software has been developed to classify the CFU automatically. However, an automated identification or classification of CFU types has become the main challenge. Most of the current software has common drawbacks, such as the expensive operating cost and complex machines. The purpose of this study is to investigate several selected convolutional neural network (CNN) pre-trained models to overcome these constraints for automated CFU classification. Prior to CFU classification, the images are acquired from mouse stem cells and categorized into three types which are CFU-erythroid (E), CFU-granulocyte/macrophage (GM) and CFU-PreB. These images are then pre-processed before being fed into CNN pre-trained models. The models adopt a deep learning neural network approach to extract informative features from the CFU images Classification performance shows that the models integrated with the pre-processing module can classify the CFUs with high accuracies and shorter computational time with 96.33% on 61 minutes and 37 seconds, respectively. Hence, this work finding could be used as the baseline reference for further research. Penerbit Universiti Kebangsaan Malaysia 2023 Article PeerReviewed application/pdf en http://journalarticle.ukm.my/22842/1/18%20%281%29.pdf Nurul Syahira Mohamad Zamani, and Hoe, Ernest Yoon Choong and Aqilah Baseri Huddin, and Wan Mimi Diyana Wan Zaki, and Zariyantey Abd Hamid, (2023) Deep learning for an automated image-based stem cell classification. Jurnal Kejuruteraan, 35 (5). pp. 1181-1189. ISSN 0128-0198 https://www.ukm.my/jkukm/volume-3505-2023/ |
| spellingShingle | Nurul Syahira Mohamad Zamani, Hoe, Ernest Yoon Choong Aqilah Baseri Huddin, Wan Mimi Diyana Wan Zaki, Zariyantey Abd Hamid, Deep learning for an automated image-based stem cell classification |
| title | Deep learning for an automated image-based stem cell classification |
| title_full | Deep learning for an automated image-based stem cell classification |
| title_fullStr | Deep learning for an automated image-based stem cell classification |
| title_full_unstemmed | Deep learning for an automated image-based stem cell classification |
| title_short | Deep learning for an automated image-based stem cell classification |
| title_sort | deep learning for an automated image-based stem cell classification |
| url | http://journalarticle.ukm.my/22842/1/18%20%281%29.pdf http://journalarticle.ukm.my/22842/ https://www.ukm.my/jkukm/volume-3505-2023/ |
| url_provider | http://journalarticle.ukm.my/ |