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All Journal Jurnal Sains dan Teknologi JOIV : International Journal on Informatics Visualization Sinkron : Jurnal dan Penelitian Teknik Informatika JURTEKSI EXPLORE Jurnal Teknologi Informasi dan Pendidikan Jurnal Teknologi Dan Sistem Informasi Bisnis Jurnal Informasi dan Teknologi Jurnal Informatika Ekonomi Bisnis Journal of Computer Scine and Information Technology Jurnal Ipteks Terapan : research of applied science and education Jurnal Sains Informatika Terapan (JSIT) EXPLORE Jurnal Komtekinfo Jurnal Teknologi Jurnal Informatika Ekonomi Bisnis JENTIK Jurnal Teknik dan teknologi tepat guna
Marfalino, Hari
Universitas Putra Indonesia YPTK Padang
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Journal : JOIV : International Journal on Informatics Visualization

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Development Extraction of Regional Features of Pleural Cavity Objects in Pneumothorax Lung X-ray Images by Dilation and Erosion Morphology Marfalino, Hari; Defit, Sarjon; Nurcahyo, Gunadi Widi
JOIV : International Journal on Informatics Visualization Vol 9, No 4 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.4.3387

Abstract

Image processing is a solution in the development of chest X-ray technology, starting from the image segmentation process as a preprocessing stage to separate the image object from the original background. Spontaneous pneumothorax (SP) is a type of air collection in the pleural cavity that develops without trauma. The diagnosis of pneumothorax has a sensitivity of approximately 25 to 75% using an anteroposterior chest x-ray, which still provides a dubious picture of pneumothorax. However, the development of the Region Feature algorithm with a new algorithm, namely RM Multy, has improved the accuracy. The RM Multy algorithm can calculate the area of the object, allowing it to produce the area of infiltration in the right lung, left lung, and the lung as a whole. The Region Feature results of the Pneumothorax obtained with the detected image area as many as 19 areas, for the pixel size of each area are 145, 355, 110, 31, 31, 52, 30, 36, 54, 122, 58, 23, 476, 77, 192, 24, 168, 263, 41 and 44. So the total pixels for 19 areas is 2301. The area converted to mm2 is 2301 x 0.04 mm2 = 92.04 mm2. Classification results on lungs with Pneumothorax and Normal by detection process with RM Multy using the CNN algorithm with an accuracy of 96.43%. This accuracy confirms the success of the system, which has been processed using a new algorithm. Therefore, further development is needed to improve detection accuracy in pneumothorax cases with smaller area sizes.
Co-Authors -, Mardison Agung Ramadhanu Agung, Maharian Assuary, Mutiara Fernandes Aulia Fitrul Hadi Azlina Mega Sari Defit , Sarjon Devi Gusmita Dinda Djesmedi Dinul Akhiyar Djesmedi, Dinda Eva Rianti Fatmasari, Nova Febria Sylvana Filresi, Afandi Iddil Firdalius, Fitri Firdaus Firna Yenila Fitri Firdalius Gunadi Widi Nurcahyo Guslendra Guslendra Guslendra Guslendra Guslendra Hadi, Aulia Fitrul Hanum, Luthfia Harkamsyah Andrianof Huriyyah Hana, Riri Ichsan Pribadi Irzal Arief Wisky Juneri, Gladio Larissa Navia Rani Larissa Rani Liana Gema, Rima Mardison Mellya Rindhani Aditia Mutiana Pratiwi Novita, Triana Pane, Ferry Juliandri Pratiwi, Mutiana Pribadi, Ichsan Putri Asysyura Yuska Randi Rani, Larissa Navia Rianti, Eva Rianti, Eva Rima Liana Gema Rima Liana Gema Ritna Wahyuni Roni, Giovan Septiyan Roni Safitri, Mesa Sarjon Defit Silfia Andini Sofika Enggari Suci Wahyuni Surmayanti Surmayanti, Surmayanti Syafri Arlis Sylvana, Febria Triana Novita Trisna, Novi Wahyu Muri Hartika Yenila, Firna Yogi Wiyandra