Authors : Shekofeh Yaraghi; Farhad Khosravi

Volume/Issue : Volume 9 - 2024, Issue 7 - July

Google Scholar : https://tinyurl.com/3yumtzdd

Scribd : https://tinyurl.com/3e4m393f

DOI : https://doi.org/10.38124/ijisrt/IJISRT24JUL1334

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Pneumonia is a life threatening disease, which occurs in the lungs caused by either bacterial or viral infection. A person suffering from pneumonia has some symptoms including cough, fever and chills, dyspnea, and low energy and appetite. The symptoms will worsen and it can be life endangering if not acted upon in the right time. Pneumonia can be diagnosed using various methods and devices, such as blood tests, sputum culture , and various types of imaging, but the most common diagnostic method is chest X-ray imaging. According to the progress achieved in the diagnosis of pneumonia, there are some problems such as the low accuracy of the diagnosis. Hence the purpose of this article is to diagnose pneumonia from chest x-ray images using transfer learning and Generative Adversarial Network (GAN) with high accuracy in two groups of normal and Pneumonia and then diagnose the type of disease in three groups: normal, viral pneumonia and bacterial pneumonia. The dataset of the article contains 5856 chest X-ray images, including normal images, viral pneumonia and bacterial pneumonia. Adversarial generator network was used in order to increase the data volume and accuracy of diagnosis. Two different pre-trained deep Convolutional Neural Network (CNN) including DenseNet121 and MobileNet, were used for deep transfer learning. The result obtained in dividing into two classes, normal and pneumonia, using DenseNet121 and MobileNet, reached an accuracy of 0.99, which is improved compared to the previous method. Therefore, the results of proposed study can be useful in faster diagnosing pneumonia by the radiologist and can help in the fast screening of the pneumonia patients.

Keywords : Pneumonia, Chest X-ray Images, Generative Adversarial Network, Deep Transfer Learning.

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