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[12]M. M. Thaha, K. P. M. Kumar, B. S. Murugan, S. Dhanasekeran, P. Vijayakarthick, and A. S. Selvi, “Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images,” J. Med. Syst., vol. 43, no. 9, p. 294, 2019, doi: 10.1007/s10916-019-1416-0.
[13]A. Desiani, M. Erwin, B. Suprihatin, S. Yahdin, A. I. Putri, and F. R. Husein, “Bi-path Architecture of CNN Segmentation and Classification Method for Cervical Cancer Disorders Based on Pap-smear Images,” IAENG Int. J. Comput. Sci., vol. 48, no. 3, pp. 1–9, 2021.
[14]R. D. P. Olvera, E. M. Zerón, J. C. P. Ortega, J. M. R. Arreguín, and E. G. Hurtado, “A Feature Extraction Using SIFT with a Preprocessing by Adding CLAHE Algorithm to Enhance Image Histograms,” Proc. -2014 IEEE Int. Conf. Mechatronics, Electron. Automot. Eng. ICMEAE 2014, pp. 20–25, 2015, doi: 10.1109/ICMEAE.2014.41.
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[16]I. Bagus, L. Mahadya, M. Sudarma, I. N. S. Kumara, and A. Optimizer, “Resonance Imaging Dengan Menggunakan Metode U-NET,” Majalah Ilmiah Teknologi Elektro, vol. 19, no. 2, pp. 151–156, 2020.
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[19]S. Sharma, S. Sharma, and A. Athaiya, “Activation Functions in Neural Networks,” Int. J. Eng. Appl. Sci. Technol., vol. 04, no. 12, pp. 310–316, 2020, doi: 10.33564/ijeast.2020.v04i12.054.
[20]J. Lin, D. Liu, H. Yang, H. Li, and F. Wu, “Convolutional Neural Network-Based Block Up-Sampling for HEVC,” IEEE Trans. Circuits Syst. Video Technol., vol. 29, no. 12, pp. 3701–3715, 2019, doi: 10.1109/TCSVT.2018.2884203.
[21]U. Ruby, P. Theerthagiri, Jacob Jeena, and Vamsidhar, “Binary cross entropy with deep learning technique for Image classification,” Int. J. Adv. Trends Comput. Sci. Eng., vol. 9, no. 4, pp. 5393–5397, 2020, doi: 10.30534/ijatcse/2020/175942020.
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[23]S. Yahdin, A. Desiani, N. Gofar, K. Agustin, and D. Rodiah, “Application of the Relief-f Algorithm for Feature Selection in the Prediction of the Relevance Education Background with the Graduate Employment of the Universitas Sriwijaya,” Comput. Eng. Appl., vol. 10, no. 2, pp. 71–80, 2021, [Online]. Available: https://comengapp.unsri.ac.id/index.php/comengapp/article/view/369.
[24]B. Oltu, B. K. Karaca, H. Erdem, and A. Özgür, “A systematic review of transfer learning based approaches for diabetic retinopathy detection,” 2021, [Online]. Available: http://arxiv.org/abs/2105.13793.
[25]D. M. W. Powers, “Evaluation Evaluation a Monte Carlo study,” pp. 1–5, 2015, [Online]. Available: http://arxiv.org/abs/1504.00854.
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[27]Y. Tang, “XLSor : A Robust and Accurate Lung Segmentor on Chest X-Rays Using Criss-Cross Attention and Customized Radiorealistic Abnormalities Generation,” pp. 457–467, 2019.
[28]L. Vidal, J. De Moura, J. Novo, and M. Ortega, “Multi-stage transfer learning for lung segmentation using portable X-ray devices for patients with COVID-19,” Expert Syst. Appl., vol. 173, p. 8, 2021, doi: 10.1016/j.eswa.2021.114677.
[2]S. Lika Aprilia, “Rontgen Thorax,” 2021. https://hellosehat.com/pernapasan-/rontgen-dada/.
[3]A. Desiani, B. Suprihatin, S. Yahdin, A. I. Putri, and F. R. Husein, “Bi -path Architecture of CNN Segmentation and Classification Method for Cervical Cancer Disorders Based on Pap -smear Images,” IAENG Int. J. Comput. Sci., vol. 48, no. 3, p. 37, 2021.
[4]H.Chung, H. Ko, S. J. Jeon, K. H. Yoon, and J. Lee, “Automatic Lung Segmentation with Juxta-Pleural Nodule Identification Using Active Contour Model and Bayesian Approach,” IEEE J. Transl. Eng. Heal. Med., vol. 6, no. October 2017, pp. 1–13, 2018, doi: 10.1109/JTEHM.2018.2837901.
[5]Z. Zhai, M. Staring, and B. C. Stoel, “Lung vessel segmentation in CT images using graph cuts,” STATS, no. March, pp. 1–7, 2016, doi: 10.1117/12.2216827.
[6]M. N. Saad and H. A. Hamid, “Image Segmentation for Lung Region in Chest X-ray Images using Edge Detection and Morphology,” IEEE Int. Conf. Controy Syst., no. November, pp. 28–30, 2014.
[7]A. Mardhiyah and A. Harjoko, “Metode Segmentasi Paru-paru dan Jantung Pada Citra X-Ray Thorax,” IJEIS (Indonesian J. Electron. Instrum. Syst., vol. 1, no. 2, pp. 35–44, 2011, doi: 10.22146/ijeis.1961.
[8]M. Wang and W. Deng, “Deep visual domain adaptation: A survey,” Neurocomputing, vol. 312, no. April, pp. 135–153, 2018, doi: 10.1016/j.neucom.2018.05.083.
[9]Y. Guo, Y. Liu, A. Oerlemans, S. Lao, S. Wu, and M. S. Lew, “Deep learning for visual understanding: A review,” Neurocomputing, vol. 187, pp. 27–48, 2016, doi: 10.1016/j.neucom.2015.09.116.
[10]S. Indolia, A. K. Goswami, S. P. Mishra, and P. Asopa, “Conceptual Understanding of Convolutional Neural Network-A Deep Learning Approach,” Procedia Comput. Sci., vol. 132, pp. 679–688, 2018, doi: 10.1016/j.procs.2018.05.069.
[11]R. Rouhi, M. Jafari, S. Kasaei, and P. Keshavarzian, “Benign and malignant breast tumors classification based on region growing and CNN segmentation,” Expert Syst. Appl., vol. 42, no. 3, pp. 990–1002, 2015, doi: 10.1016/j.eswa.2014.09.020.
[12]M. M. Thaha, K. P. M. Kumar, B. S. Murugan, S. Dhanasekeran, P. Vijayakarthick, and A. S. Selvi, “Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images,” J. Med. Syst., vol. 43, no. 9, p. 294, 2019, doi: 10.1007/s10916-019-1416-0.
[13]A. Desiani, M. Erwin, B. Suprihatin, S. Yahdin, A. I. Putri, and F. R. Husein, “Bi-path Architecture of CNN Segmentation and Classification Method for Cervical Cancer Disorders Based on Pap-smear Images,” IAENG Int. J. Comput. Sci., vol. 48, no. 3, pp. 1–9, 2021.
[14]R. D. P. Olvera, E. M. Zerón, J. C. P. Ortega, J. M. R. Arreguín, and E. G. Hurtado, “A Feature Extraction Using SIFT with a Preprocessing by Adding CLAHE Algorithm to Enhance Image Histograms,” Proc. -2014 IEEE Int. Conf. Mechatronics, Electron. Automot. Eng. ICMEAE 2014, pp. 20–25, 2015, doi: 10.1109/ICMEAE.2014.41.
[15]W. S. Eka Putra, “Klasifikasi Citra Menggunakan Convolutional Neural Network (CNN) pada Caltech 101,” J. Tek. ITS, vol. 5, no. 1, 2016, doi: 10.12962/j23373539.v5i1.15696.
[16]I. Bagus, L. Mahadya, M. Sudarma, I. N. S. Kumara, and A. Optimizer, “Resonance Imaging Dengan Menggunakan Metode U-NET,” Majalah Ilmiah Teknologi Elektro, vol. 19, no. 2, pp. 151–156, 2020.
[17]S. Albawi, T. A. M. Mohammed, and S. Alzawi, “Layers of a Convolutional Neural Network,” Ieee, p. 16, 2017.
[18]W. Chen, B. Yang, J. Li, and J. Wang, “An approach to detecting diabetic retinopathy based on integrated shallow convolutional neural networks,” IEEE Access, vol. 8, pp. 178552–178562, 2020, doi: 10.1109/ACCESS.2020.3027794.
[19]S. Sharma, S. Sharma, and A. Athaiya, “Activation Functions in Neural Networks,” Int. J. Eng. Appl. Sci. Technol., vol. 04, no. 12, pp. 310–316, 2020, doi: 10.33564/ijeast.2020.v04i12.054.
[20]J. Lin, D. Liu, H. Yang, H. Li, and F. Wu, “Convolutional Neural Network-Based Block Up-Sampling for HEVC,” IEEE Trans. Circuits Syst. Video Technol., vol. 29, no. 12, pp. 3701–3715, 2019, doi: 10.1109/TCSVT.2018.2884203.
[21]U. Ruby, P. Theerthagiri, Jacob Jeena, and Vamsidhar, “Binary cross entropy with deep learning technique for Image classification,” Int. J. Adv. Trends Comput. Sci. Eng., vol. 9, no. 4, pp. 5393–5397, 2020, doi: 10.30534/ijatcse/2020/175942020.
[22]K. H. Mahmud et al., “Classification of plant leaf diseases based on improved convolutional neural network,” Neurocomputing, vol. 3, no. 1, pp. 467–488, 2019, doi: 10.1016/j.eswa.2018.11.008.
[23]S. Yahdin, A. Desiani, N. Gofar, K. Agustin, and D. Rodiah, “Application of the Relief-f Algorithm for Feature Selection in the Prediction of the Relevance Education Background with the Graduate Employment of the Universitas Sriwijaya,” Comput. Eng. Appl., vol. 10, no. 2, pp. 71–80, 2021, [Online]. Available: https://comengapp.unsri.ac.id/index.php/comengapp/article/view/369.
[24]B. Oltu, B. K. Karaca, H. Erdem, and A. Özgür, “A systematic review of transfer learning based approaches for diabetic retinopathy detection,” 2021, [Online]. Available: http://arxiv.org/abs/2105.13793.
[25]D. M. W. Powers, “Evaluation Evaluation a Monte Carlo study,” pp. 1–5, 2015, [Online]. Available: http://arxiv.org/abs/1504.00854.
[26]Y. Wei, G. Shen, and J. J. Li, “A fully automatic method for lung parenchyma segmentation and repairing,” J. Digit. Imaging, vol. 26, no. 3, pp. 483–495, 2013, doi: 10.1007/s10278-012-9528-9.
[27]Y. Tang, “XLSor : A Robust and Accurate Lung Segmentor on Chest X-Rays Using Criss-Cross Attention and Customized Radiorealistic Abnormalities Generation,” pp. 457–467, 2019.
[28]L. Vidal, J. De Moura, J. Novo, and M. Ortega, “Multi-stage transfer learning for lung segmentation using portable X-ray devices for patients with COVID-19,” Expert Syst. Appl., vol. 173, p. 8, 2021, doi: 10.1016/j.eswa.2021.114677.
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Affiliations
Teddi Pranata
Universitas Sriwijaya
Anita Desiani
Universitas Sriwijaya
Bambang Suprihatin
Affiliation not stated
Herlina Hanum
Affiliation not stated
Filda Efriliyanti
Affiliation not stated
Segmentation of the Lungs on X-Ray Thorax Images with the U-Net CNN Architecture
Abstract
Lungs are one of the most important parts of the human body. They are very susceptible to various disorders and diseases. For this reason, it is necessary to detect or diagnose the lungs. In this study, we present a method for lung segmentation using the CNN method U-Net architecture. The initial stage was pre-processed did a 1-1 correspondence to equalize the amount of training data and testing data and resized the image so all images have the same size. The process continued with the CLAHE (Contrast Limited Adaptive Histogram Equalization), and after that, the segmentation process was carried out according to the method. This study used a dataset from the Kaggle website. The results used the CNN method of the U-Net architecture in data get an average accuracy of 91.68%, sensitivity 92.80%, and specificity 89.15%, precision 95.07, and F1-Score 93. 92%. Based on the performance evaluation results, it was concluded that the method proposed in the study is great and valid in the lungs segmentation on X-Ray Thorax images.