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(Aisyah University, Indonesia)(2) Retno Ariza Soeprihatini
(Lampung University, Indonesia)(3) Sfenrianto Sfenrianto
(Binus University, Indonesia, Indonesia)(4) Zulvi Wiyanti
(Prima Nusantara Bukittinggi University, Indonesia)(5) * Panji Bintoro
(Aisyah University, Indonesia)(6) Fitriana Fitriana
(Aisyah University, Indonesia)(7) Sukarni Sukarni
(Aisyah University, Indonesia, Indonesia)(8) Nopi Anggista Putri
(Aisyah University, Indonesia)(9) Dwi Yana Ayu Andini
(Aisyah University, Indonesia)*corresponding author
AbstractThe 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.
KeywordsExpert System; Lung Disease Diagnosis; Convolutional Neural Network; Support Vector Machine
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DOIhttps://doi.org/10.29099/ijair.v7i1.870 |
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