A Novel Transfer Learning Based Approach for Pneumonia Detection in Chest X-ray Images

Pneumonia is among the top diseases which cause most of the deaths all over the world. Virus, bacteria and fungi can all cause pneumonia. However, it is difficult to judge the pneumonia just by looking at chest X-rays. The aim of this study is to simplify the pneumonia detection process for experts...

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Bibliographic Details
Published in:	Applied sciences Vol. 10; no. 2; p. 559
Main Authors:	Chouhan, Vikash, Singh, Sanjay Kumar, Khamparia, Aditya, Gupta, Deepak, Tiwari, Prayag, Moreira, Catarina, Damaševičius, Robertas, de Albuquerque, Victor Hugo C.
Format:	Journal Article
Language:	English
Published:	Basel MDPI AG 01-01-2020
Subjects:	Chest Chronic obstructive pulmonary disease Classification computer-aided diagnosis Concept learning Deep learning Disease Fungi Health care facilities Lungs medical image processing Medical imaging Model accuracy Mortality Neural networks Pneumonia Transfer learning Viruses X-rays United States > US
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Summary:	Pneumonia is among the top diseases which cause most of the deaths all over the world. Virus, bacteria and fungi can all cause pneumonia. However, it is difficult to judge the pneumonia just by looking at chest X-rays. The aim of this study is to simplify the pneumonia detection process for experts as well as for novices. We suggest a novel deep learning framework for the detection of pneumonia using the concept of transfer learning. In this approach, features from images are extracted using different neural network models pretrained on ImageNet, which then are fed into a classifier for prediction. We prepared five different models and analyzed their performance. Thereafter, we proposed an ensemble model that combines outputs from all pretrained models, which outperformed individual models, reaching the state-of-the-art performance in pneumonia recognition. Our ensemble model reached an accuracy of 96.4% with a recall of 99.62% on unseen data from the Guangzhou Women and Children’s Medical Center dataset.
ISSN:	2076-3417 2076-3417
DOI:	10.3390/app10020559