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  3. Vol. 9, No. 1, February 2024
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Vol. 9, No. 1, February 2024

Issue Published : Feb 28, 2024
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Thorax X-ray Image Segmentation Technique Using Four Variants of Thresholding Algorithm

https://doi.org/10.22219/kinetik.v9i1.1809
Rio Subandi
Universitas Ahmad Dahlan
Herman
Universitas Ahmad Dahlan
Anton Yudhana
Universitas Ahmad Dahlan

Corresponding Author(s) : Rio Subandi

riosubandi9@gmail.com

Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, Vol. 9, No. 1, February 2024
Article Published : Feb 28, 2024

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Abstract

Pneumonia is a respiratory infection caused by bacteria, viruses or fungi, and has been recognized as a fairly common and threatening disease. When diagnosing this disease, doctors usually also use thorax X-ray images. Nowadays, diagnosing pneumonia has been made possible with the help of machine learning technology. Doctors or medical personnel in locations where there are no pulmonary specialists or experts can be assisted by this technology. Machine learning algorithms are used to process digital images that have passed the pre-processing and segmentation stages. This paper offers a solution to segmentation technique of thorax X-ray digital image using a combination of four thresholding algorithms. This combination aims to find the best CNN model with segmentation techniques in the form of the most suitable thresholding algorithm. The result of this research is four different data sets. The thresholding algorithms used include binary, thresh binary inv, thresh to zero, thresh tozero inv with a threshold value of 150. The data used in this research is a thorax X-ray image dataset, as many as 5,856 images acquired from the Kaggle repository data. The program code in this research uses the Python programming language in the Anaconda environment. This research has resulted in a comparison of the accuracy values obtained using 4 variants between thres_binary thresholding algorithm and thres_binary_inv. The thres_tozero obtained 95% of accuracy while thres_tozero_inv obtained 94% of accuracy.

Keywords

Pneumonia Segmentation Thresholding
Subandi, R., Herman, & Yudhana, A. (2024). Thorax X-ray Image Segmentation Technique Using Four Variants of Thresholding Algorithm. Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, 9(1), 9-20. https://doi.org/10.22219/kinetik.v9i1.1809
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References
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References


T. Gabruseva, D. Poplavskiy, dan A. Kalinin, “Deep learning for automatic pneumonia detection,” IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. Work., vol. 2020-June, hal. 1436–1443, 2020. https://doi.org/10.1109/CVPRW50498.2020.00183

A. U. Ibrahim, M. Ozsoz, S. Serte, F. Al-Turjman, dan P. S. Yakoi, “Pneumonia Classification Using Deep Learning from Chest X-ray Images During COVID-19,” Cognit. Comput., no. 0123456789, 2021. https://doi.org/10.1007/s12559-020-09787-5

N. Chebib dkk., “Pneumonia prevention in the elderly patients: the other sides,” Aging Clin. Exp. Res., vol. 33, no. 4, hal. 1091–1100, 2021. https://doi.org/10.1007/s40520-019-01437-7

D. Varshni, K. Thakral, L. Agarwal, R. Nijhawan, dan A. Mittal, “Pneumonia Detection Using CNN based Feature Extraction,” Proc. 2019 3rd IEEE Int. Conf. Electr. Comput. Commun. Technol. ICECCT 2019, hal. 1–7, 2019. https://doi.org/10.1109/ICECCT.2019.8869364

Q. Wang, D. Yang, Z. Li, X. Zhang, dan C. Liu, “Deep regression via multi-channel multi-modal learning for pneumonia screening,” IEEE Access, vol. 8, hal. 78530–78541, 2020. https://doi.org/10.1109/ACCESS.2020.2990423

E. Ayan dan H. Murat, “Diagnosis of Pneumonia from Chest X-Ray Images using Deep Learning,” hal. 0–4, 2019.

H. Ren dkk., “Interpretable Pneumonia Detection by Combining Deep Learning and Explainable Models with Multisource Data,” IEEE Access, vol. 9, hal. 95872–95883, 2021. https://doi.org/10.1109/ACCESS.2021.3090215

I. Bakti, M. Firdaus, T. Informasi, dan T. Industri, “Klasifikasi File Gambar Hasil X-Ray Paru -Paru Dengan Arsitektur Convolution Neural Network ( CNN ),” J. Inf. Technol., vol. 3, no. 1, hal. 26–34, 2023.

L. Hernando dan A. Avaldo, “Implementasi Fuzzy Logic pada Alat Pemisah Buah Tomat,” J. Sains dan Inform., vol. 4, no. 1, hal. 55–61, 2022. https://doi.org/10.22216/jsi.v8i2.1637

R. Shinta, Jasril, M. Irsyad, F. Yanto, dan S. Sanjaya, “Klasifikasi Citra Penyakit Daun Tanaman Padi Menggunakan CNN dengan Arsitektur VGG-19 Rahma,” J. Sains dan Inform., vol. 4, no. 1, hal. 37–45, 2018. https://doi.org/10.22216/jsi.v9i1.2175

J. Yopento, E. Ernawati, dan F. F. Coastera, “Identifikasi Pneumonia Pada Citra X-Ray Paru-Paru Menggunakan Metode Convolutional Neural Network (CNN) Berdasarkan Ekstraksi Fitur Sobel,” Rekursif J. Inform., vol. 10, no. 1, hal. 40–47, 2022. https://doi.org/10.33369/rekursif.v10i1.17247

Z. Luo, W. Yang, Y. Yuan, R. Gou, dan X. Li, “Semantic segmentation of agricultural images: A survey,” Inf. Process. Agric., no. xxxx, hal. 1–15, 2023. https://doi.org/10.1016/j.inpa.2023.02.001

J. M. Adam dkk., “Deep learning-based semantic segmentation of urban-scale 3D meshes in remote sensing: A survey,” Int. J. Appl. Earth Obs. Geoinf., vol. 121, no. June, hal. 103365, 2023. https://doi.org/10.1016/j.jag.2023.103365

M. Hasan Jahid, M. Alom Shahin, dan M. Ali Shikhar, “Deep Learning based Detection and Segmentation of COVID-19 Pneumonia on Chest X-ray Image,” 2021 Int. Conf. Inf. Commun. Technol. Sustain. Dev. ICICT4SD 2021 - Proc., hal. 210–214, 2021. https://doi.org/10.1109/ICICT4SD50815.2021.9396878

R. Zhang, G. Li, T. Wunderlich, dan L. Wang, “A survey on deep learning-based precise boundary recovery of semantic segmentation for images and point clouds,” Int. J. Appl. Earth Obs. Geoinf., vol. 102, hal. 102411, 2021. https://doi.org/10.1016/j.jag.2021.102411

S. Wang, D. M. Yang, R. Rong, X. Zhan, dan G. Xiao, “Pathology Image Analysis Using Segmentation Deep Learning Algorithms,” Am. J. Pathol., vol. 189, no. 9, hal. 1686–1698, 2019. https://doi.org/10.1016/j.ajpath.2019.05.007

R. F. Nugrohoputri dkk., “Segmentasi Citra Nukleus Sel Kanker Serviks Menggunakan Otsu Thresholding dan Morfologi Closing,” JSI J. …, vol. 14, no. 1, hal. 2533–2543, 2022.

A. Yudhana, R. Umar, dan S. Saputra, “Fish Freshness Identification Using Machine Learning: Performance Comparison of k-NN and Naïve Bayes Classifier,” J. Comput. Sci. Eng., vol. 16, no. 3, hal. 153–164, 2022. https://doi.org/10.5626/JCSE.2022.16.3.153

R. T. Wahyuningrum dkk., “Segmentasi Citra X-Ray Dada Menggunakan Metode Modifikasi Deeplabv3+,” vol. 10, no. 3, hal. 687–698, 2023. https://doi.org/10.25126/jtiik.20231036754

S. Saputra, A. Yudhana, dan R. Umar, “Implementation of Naïve Bayes for Fish Freshness Identification Based on Image Processing,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 6, no. 3, hal. 412–420, 2022. https://doi.org/10.29207/resti.v6i3.4062

M. Furqan, A. Aulia, dan Sriani, “Algoritma K-Means Untuk Segmentasi Kematangan Buah Jeruk Berdasarkan Kemiripan Warna,” J. Sains Komput. Inform., vol. 6, no. 1, hal. 199–208, 2022.

I. K. Adi Bayu Adnyana, I. M. Oka Widyantara, dan N. Dewi Wirastuti, “Analisa Metode Shannon Entropy Dan Differential Evolution Untuk Kompresi Gambar,” J. SPEKTRUM, vol. 8, no. 2, hal. 221, 2021. https://doi.org/10.24843/SPEKTRUM.2021.v08.i02.p25

A. Yudhana, Sunardi, dan S. Saifullah, “Segmentation comparing eggs watermarking image and original image,” Bull. Electr. Eng. Informatics, vol. 6, no. 1, hal. 47–53, 2017. https://doi.org/10.11591/eei.v6i1.595

P. K. Sahoo, S. Soltani, dan A. K. C. Wong, “A survey of thresholding techniques,” Comput. Vision, Graph. Image Process., vol. 41, no. 2, hal. 233–260, 1988. https://doi.org/10.1016/0734-189X(88)90022-9

N. Nafi’iyah dan E. Setyati, “Lung X-Ray Image Enhancement to Identify Pneumonia with CNN,” East Indones. Conf. Comput. Inf. Technol., hal. 421–426, 2021.

A. Fadlil dan D. Prayogi, “Sistem Pengenalan Wajah pada Keamanan Ruangan Berbasis Convolutional Neural Network,” vol. 6, no. September, hal. 636–647, 2022.

N. Abdillah, A. K. W. Hapantenda, A. Habib, dan I. Listiowarni, “Klasifikasi Viral Pneumonia Mengunakan Metode Convolutional Neural Network Dan Support Vector Machine,” Konvergensi, vol. 18, no. 2, hal. 50–56, 2022. https://doi.org/10.30996/konv.v18i1.6916

B. Boehm, Barry Boehm Software Engineering Economics, vol. 10, no. 1. Prentice-hall, 1984.

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