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Thorax X-ray Image Segmentation Technique Using Four Variants of Thresholding Algorithm
Corresponding Author(s) : Rio Subandi
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control,
Vol. 9, No. 1, February 2024
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.
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References
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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
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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
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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.