An enhanced classification of bacteria pathogen on microscopy images using deep learning
Classification of bacteria pathogens has significant importance issues in the clinical microbiology field. The taxonomy identification of bacteria is usually recognized through microscopy imaging. The classical procedure has the lacks detection and a high misclassification rate. Recently, computer-a...
| Main Authors: | , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English English |
| Published: |
IEEE
2021
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/37877/ http://umpir.ump.edu.my/id/eprint/37877/1/An%20enhanced%20classification%20of%20bacteria%20pathogen%20on%20microscopy%20images_FULL.pdf http://umpir.ump.edu.my/id/eprint/37877/2/An%20enhanced%20classification%20of%20bacteria%20pathogen%20on%20microscopy%20images%20.pdf |
| _version_ | 1848825369402015744 |
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| author | Son Ali, Akbar Kamarul Hawari, Ghazali Habsah, Hasan Zeehaida, Mohamed Wahyu Sapto, Aji |
| author_facet | Son Ali, Akbar Kamarul Hawari, Ghazali Habsah, Hasan Zeehaida, Mohamed Wahyu Sapto, Aji |
| author_sort | Son Ali, Akbar |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | Classification of bacteria pathogens has significant importance issues in the clinical microbiology field. The taxonomy identification of bacteria is usually recognized through microscopy imaging. The classical procedure has the lacks detection and a high misclassification rate. Recently, computer-aided detection is an applied deep learning approach that has been growing to improve classification quality. This study proposed an enhanced classification technique to recognize the bacterial pathogen images. The DensNet201 pre-trained CNN architecture has been used for deep feature extraction and classification. In addition, the transfer learning with the freeze layer technique applied can enhance the accuracy performance and reduce the false-positive rate. The experimental result can improve state-of-the-art decision-making. |
| first_indexed | 2025-11-15T03:27:50Z |
| format | Conference or Workshop Item |
| id | ump-37877 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-15T03:27:50Z |
| publishDate | 2021 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-378772023-06-27T03:40:16Z http://umpir.ump.edu.my/id/eprint/37877/ An enhanced classification of bacteria pathogen on microscopy images using deep learning Son Ali, Akbar Kamarul Hawari, Ghazali Habsah, Hasan Zeehaida, Mohamed Wahyu Sapto, Aji QH Natural history RB Pathology TK Electrical engineering. Electronics Nuclear engineering Classification of bacteria pathogens has significant importance issues in the clinical microbiology field. The taxonomy identification of bacteria is usually recognized through microscopy imaging. The classical procedure has the lacks detection and a high misclassification rate. Recently, computer-aided detection is an applied deep learning approach that has been growing to improve classification quality. This study proposed an enhanced classification technique to recognize the bacterial pathogen images. The DensNet201 pre-trained CNN architecture has been used for deep feature extraction and classification. In addition, the transfer learning with the freeze layer technique applied can enhance the accuracy performance and reduce the false-positive rate. The experimental result can improve state-of-the-art decision-making. IEEE 2021 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/37877/1/An%20enhanced%20classification%20of%20bacteria%20pathogen%20on%20microscopy%20images_FULL.pdf pdf en http://umpir.ump.edu.my/id/eprint/37877/2/An%20enhanced%20classification%20of%20bacteria%20pathogen%20on%20microscopy%20images%20.pdf Son Ali, Akbar and Kamarul Hawari, Ghazali and Habsah, Hasan and Zeehaida, Mohamed and Wahyu Sapto, Aji (2021) An enhanced classification of bacteria pathogen on microscopy images using deep learning. In: 4th International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2021 , 16 December 2021 , Virtual, Yogyakarta. 119 -123.. ISBN 978-166540151-7 (Published) https://doi.org/10.1109/ISRITI54043.2021.9702809 |
| spellingShingle | QH Natural history RB Pathology TK Electrical engineering. Electronics Nuclear engineering Son Ali, Akbar Kamarul Hawari, Ghazali Habsah, Hasan Zeehaida, Mohamed Wahyu Sapto, Aji An enhanced classification of bacteria pathogen on microscopy images using deep learning |
| title | An enhanced classification of bacteria pathogen on microscopy images using deep learning |
| title_full | An enhanced classification of bacteria pathogen on microscopy images using deep learning |
| title_fullStr | An enhanced classification of bacteria pathogen on microscopy images using deep learning |
| title_full_unstemmed | An enhanced classification of bacteria pathogen on microscopy images using deep learning |
| title_short | An enhanced classification of bacteria pathogen on microscopy images using deep learning |
| title_sort | enhanced classification of bacteria pathogen on microscopy images using deep learning |
| topic | QH Natural history RB Pathology TK Electrical engineering. Electronics Nuclear engineering |
| url | http://umpir.ump.edu.my/id/eprint/37877/ http://umpir.ump.edu.my/id/eprint/37877/ http://umpir.ump.edu.my/id/eprint/37877/1/An%20enhanced%20classification%20of%20bacteria%20pathogen%20on%20microscopy%20images_FULL.pdf http://umpir.ump.edu.my/id/eprint/37877/2/An%20enhanced%20classification%20of%20bacteria%20pathogen%20on%20microscopy%20images%20.pdf |