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Open AccessArticle

Efficient Pneumonia Detection in Chest Xray Images Using Deep Transfer Learning

1
Department of Electronics and Communication Engineering, National Institute of Technology, Warangal 506004, India
2
Department of Electronics and Communication Engineering, Harcourt Butler Technical University, Kanpur 208002, India
3
Department of Electronics and Communication Engineering, Visvesvaraya National Institute of Technology, Nagpur 440010, India
4
Department of Engineering-Renewable Energy and Thermodynamics, Aarhus University, 8000 Aarhus, Denmark
5
Department of Energy IT, Gachon University, Seongnam 13120, Korea
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Diagnostics 2020, 10(6), 417; https://doi.org/10.3390/diagnostics10060417
Received: 17 May 2020 / Revised: 13 June 2020 / Accepted: 16 June 2020 / Published: 19 June 2020
(This article belongs to the Section Machine Learning and Artificial Intelligence in Diagnostics)
Pneumonia causes the death of around 700,000 children every year and affects 7% of the global population. Chest X-rays are primarily used for the diagnosis of this disease. However, even for a trained radiologist, it is a challenging task to examine chest X-rays. There is a need to improve the diagnosis accuracy. In this work, an efficient model for the detection of pneumonia trained on digital chest X-ray images is proposed, which could aid the radiologists in their decision making process. A novel approach based on a weighted classifier is introduced, which combines the weighted predictions from the state-of-the-art deep learning models such as ResNet18, Xception, InceptionV3, DenseNet121, and MobileNetV3 in an optimal way. This approach is a supervised learning approach in which the network predicts the result based on the quality of the dataset used. Transfer learning is used to fine-tune the deep learning models to obtain higher training and validation accuracy. Partial data augmentation techniques are employed to increase the training dataset in a balanced way. The proposed weighted classifier is able to outperform all the individual models. Finally, the model is evaluated, not only in terms of test accuracy, but also in the AUC score. The final proposed weighted classifier model is able to achieve a test accuracy of 98.43% and an AUC score of 99.76 on the unseen data from the Guangzhou Women and Children’s Medical Center pneumonia dataset. Hence, the proposed model can be used for a quick diagnosis of pneumonia and can aid the radiologists in the diagnosis process. View Full-Text
Keywords: pneumonia; chest X-ray images; convolution neural network (CNN); deep learning; transfer learning; computer-aided diagnostics pneumonia; chest X-ray images; convolution neural network (CNN); deep learning; transfer learning; computer-aided diagnostics
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Hashmi, M.F.; Katiyar, S.; Keskar, A.G.; Bokde, N.D.; Geem, Z.W. Efficient Pneumonia Detection in Chest Xray Images Using Deep Transfer Learning. Diagnostics 2020, 10, 417.

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