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DOI: 10.14569/IJACSA.2021.0121084
PDF

Efficient DNN Ensemble for Pneumonia Detection in Chest X-ray Images

Author 1: V S Suryaa
Author 2: Arockia Xavier Annie R
Author 3: Aiswarya M S

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 12 Issue 10, 2021.

  • Abstract and Keywords
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Abstract: Pneumonia is a disease caused by a variety of organisms, including bacteria, viruses, and fungi, which could be fatal if timely medical care is not provided. According to the World Health Organization (WHO) report, the most common diagnosis for severe COVID-19 is severe pneumonia. The most common method of detecting Pneumonia is through chest X-ray which is a very time intensive process and requires a skilled expert. The rapid development in the field of deep learning and neural networks in recent years has led to drastic improvement in automation of pneumonia detection from analysing chest x- rays. In this paper, a pre-trained Convolutional Neural Networks (CNN) on chest x-ray images is used as feature extractors which are then further processed to classify the images in order to predict whether a person has pneumonia or not. The different pre- trained Convolutional Neural Networks used are assessed with various parameters regarding their predictions on the images. The results of pre-trained neural networks were examined, and an ensemble model was proposed that combines the predictions of the best pre-trained models to produce better results than individual models.

Keywords: Deep neural networks; ensemble learning; pneumonia detection using x-ray images; transfer learning

V S Suryaa, Arockia Xavier Annie R and Aiswarya M S, “Efficient DNN Ensemble for Pneumonia Detection in Chest X-ray Images” International Journal of Advanced Computer Science and Applications(IJACSA), 12(10), 2021. http://dx.doi.org/10.14569/IJACSA.2021.0121084

@article{Suryaa2021,
title = {Efficient DNN Ensemble for Pneumonia Detection in Chest X-ray Images},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2021.0121084},
url = {http://dx.doi.org/10.14569/IJACSA.2021.0121084},
year = {2021},
publisher = {The Science and Information Organization},
volume = {12},
number = {10},
author = {V S Suryaa and Arockia Xavier Annie R and Aiswarya M S}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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