Detection and Counting Bacilli Tuberculosis Images using Classical Image Processing and Deep Learning-Reference-Cited by-同舟云学术

Detection and Counting Bacilli Tuberculosis Images using Classical Image Processing and Deep Learning

Published:2024-06-21 Issue: Volume:26 Page:47-52
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Container-title:2024 2nd International Conference on Communications, Computing and Artificial Intelligence
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Author:

Yuniarti Heny¹^ORCID,Fatichah Chastine²^ORCID,Sigit Riyanto³^ORCID

Affiliation:

1. Department of Informatics, Faculty of Intelligent Electrical and Informatics Technology, Institut Teknologi Sepuluh Nopember, Indonesia and Department of Computer Engineering, Politeknik Elektronika Negeri Surabaya, Indonesia

2. Department of Informatics, Faculty of Intelligent Electrical and Informatics Technology, Institut Teknologi Sepuluh Nopember, Indonesia

3. Department of Computer Engineering, Politeknik Elektronika Negeri Surabaya, Indonesia

Funder

Beasiswa Pendidikan Indonesia (BPI), Lembaga Pengelola Dana Pendidikan (LPDP)

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3676581.3676589

Reference16 articles.

1. Pangestu, N., A., Sigit, R., Harsono, T., Wahyunitisari, M., R., Anwar, A., and Yunitasari, D., A., “Classification and Counting of Mycobacterium Tuberculosis from Sputum Microscopic Image using Fuzzy Logic”, Internasional Electronics Symposium (IES), 2020.

2. Diagnosis and counting of tuberculosis bacilli using digital image processing

3. Detection of Tubercle Bacilli by Fusion with YOLOv5s and ESRGAN

4. Automatic Detection of Tuberculosis Bacilli from Microscopic Sputum Smear Images Using Faster R-CNN, Transfer Learning and Augmentation

5. Detection of Mycobacterium Tuberculosis in Ziehl-Neelsen Sputum Smear Images

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