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All Journal Bulletin of Electrical Engineering and Informatics International Journal of Artificial Intelligence Research Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Jurnal Ilmiah Keperawatan Sai Betik Aisyah Journal of Informatics and Electrical Engineering Jurnal Aisyah : Jurnal Ilmu Kesehatan Jurnal Pengabdian Kepada Masyarakat Ungu ( Abdi Ke Ungu) Jurnal Ilmu Kesehatan Indonesia (JIKSI)
Fitriana Fitriana
Universitas Aisyah Pringsewu
Computer Science & IT Education Electrical & Electronics Engineering Engineering Health Professions Medicine & Pharmacology Nursing Public Health Veterinary Other

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Expert System for Diagnosis of Lung Disease from X-Ray Using CNN and SVM Zulkifli, Zulkifli; Soeprihatini, Retno Ariza; Sfenrianto, Sfenrianto; Wiyanti, Zulvi; Bintoro, Panji; Fitriana, Fitriana; Sukarni, Sukarni; Putri, Nopi Anggista; Andini, Dwi Yana Ayu
International Journal of Artificial Intelligence Research Vol 7, No 2 (2023): December 2023
Publisher : STMIK Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v7i1.870

Abstract

The lung disease diagnosis expert system utilizes human knowledge to diagnose various conditions affecting the lung. Diseases caused by fungal or bacterial infection in the organ can cause inflammation as well as death when it is not detected on time. A standard method to diagnose these conditions is the use of a chest X-ray (CXR), which requires careful examination of the image by an expert. In this study, several CNN and SVM architectural models were proposed to classify CXR images to diagnose whether a person has COVID-19, Viral Pneumonia, Bacterial Pneumonia, Tuberculosis (TB), and Normal. The experiment showed that InceptionV3 had the best results compared to other CNN architectures and SVM. Classification accuracy, precision, recall, and f1-score of CXR images for COVID-19, Viral Pneumonia, Bacterial Pneumonia, TB, and Normal were 0.86, 0.91, 0.91, and 0.91, respectively. This study was based on a deep learning system with different CNN and SVM architectures that can work well on the CXR images dataset for diagnosing lung disease.
Co-Authors Abdullah Abdullah Andini, Dwi Yana Ayu Ani Kristianingsih Azri Afdhalluddin Beniqna Maharani Bertalina Bertalina Bintoro, Panji Dewi Yunarti Fauziah, Nur Alfi Hellen Febriyanti Heni Pujiastuti Ika Fitria Elmeida Komalasari Komalasari Lia Puspita Maulia Isnaini Monica Dara Delia Suja Nopi Anggista Nopi Anggista Putri Nopi Anggista Putri, Nopi Anggista Nurul Badriyah Psiari Kusuma Wardani Saras Cipta Casmi Setiani Setiani Sfenrianto, Sfenrianto Soeprihatini, Retno Ariza Sri Lita Yani Suci Lanavia Sudarmi Sudarmi Sukarni Sukarni Sukarni Sukarni Sukarni Sulistiawati, Yuni Tahta Herdian Andika Wiyanti, Zulvi Yuni Sulistyawati Yuniastini Yuniastini Yunita Anggriani Zulkifli