Pneumonia classification from chest x-ray images using pre-trained network architectures

Aqsa Shahzad; Muhammad Asad Arshed; Farrukh Liaquat; Muhammad Tanveer; Mahmood Hussain; Rabbia Alamdar

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Pneumonia classification from chest x-ray images using pre-trained network architectures

Author(s)

Aqsa Shahzad Department of Computer Science, University of Engineering & Technology,Lahore,Pakistan

Muhammad Asad Arshed School of Systems and Technology, University of Management & Technology,Lahore,Pakistan

Farrukh Liaquat School of Systems and Technology, University of Management & Technology,Lahore,Pakistan

Muhammad Tanveer School of Systems and Technology, University of Management & Technology,Lahore,Pakistan

Mahmood Hussain School of Systems and Technology, University of Management & Technology,Lahore,Pakistan

Rabbia Alamdar Department of Computer Science, National College of Business Administration & Economics,,Pakistan

Abstract

Pneumonia is a serious disease caused by a lung infection that affects young and old people and approximately cause of 4 million deaths each year. Patients that are facing disorders such as weak immune systems, asthma, and babies all are at risk specifically if pneumonia is not detected at an early stage. An early diagnosis of pneumonia is required to plan a potential treatment strategy to control and treat the condition. The objective of this study is to analyze chest radiograph images to identify lung abnormalities using pre-trained architecture. After extracting features from the images using convolutional neural network models that have been pre-trained on a large dataset called ImageNet, they are typically passed through a classifier for further processing and diagnosis. Pre-trained networks variants including VGG-16, VGG-19, DenseNet121, ResNet50, and InceptionV3 architecture were utilized in this study & results show that VGG-16 architecture performance is efective with a test accuracy of 90% and validation accuracy of 93.98% than other pre-trained architectures.

Publication Details

Page(s) 34-44

DOI DOI not available

Published Journal: VAWKUM Transactions on Computer Sciences, Volume: 10, Issue: 2, Year: 2022

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Keywords

Classification, pneumonia, Deep learning, Fine- Tuning, Pre-Trained Models, Chest X-ray Images

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