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. 2020 Aug 28;10(9):649.
doi: 10.3390/diagnostics10090649.

Deep-Pneumonia Framework Using Deep Learning Models Based on Chest X-Ray Images

Nada M Elshennawy  1 , Dina M Ibrahim  1   2
Affiliations

Deep-Pneumonia Framework Using Deep Learning Models Based on Chest X-Ray Images

Nada M Elshennawy et al. Diagnostics (Basel). .

Abstract

Pneumonia is a contagious disease that causes ulcers of the lungs, and is one of the main reasons for death among children and the elderly in the world. Several deep learning models for detecting pneumonia from chest X-ray images have been proposed. One of the extreme challenges has been to find an appropriate and efficient model that meets all performance metrics. Proposing efficient and powerful deep learning models for detecting and classifying pneumonia is the main purpose of this work. In this paper, four different models are developed by changing the used deep learning method; two pre-trained models, ResNet152V2 and MobileNetV2, a Convolutional Neural Network (CNN), and a Long Short-Term Memory (LSTM). The proposed models are implemented and evaluated using Python and compared with recent similar research. The results demonstrate that our proposed deep learning framework improves accuracy, precision, F1-score, recall, and Area Under the Curve (AUC) by 99.22%, 99.43%, 99.44%, 99.44%, and 99.77%, respectively. As clearly illustrated from the results, the ResNet152V2 model outperforms other recently proposed works. Moreover, the other proposed models-MobileNetV2, CNN, and LSTM-CNN-achieved results with more than 91% in accuracy, recall, F1-score, precision, and AUC, and exceed the recently introduced models in the literature.

Keywords: CNN; LSTM; chest X-ray image; deep learning; detecting pneumonia.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
The proposed deep learning framework for pneumonia diagnosis.
Figure 2
Figure 2
Proposed CNN architecture.
Figure 3
Figure 3
The main function code of the CNN model.
Figure 4
Figure 4
Proposed LSTM-CNN architecture.
Figure 5
Figure 5
The main function code of the LSTM-CNN model.
Figure 6
Figure 6
ResNet152V2 architecture.
Figure 7
Figure 7
MobileNetV2 architecture.
Figure 8
Figure 8
The main function code of the ResNet152V2 model.
Figure 9
Figure 9
The main function code of the MobileNetV2 model.
Figure 10
Figure 10
Chest X-ray images: (a) Normal images with and without a deep dream filter; (b) pneumonia images with and without a deep dream filter.
Figure 11
Figure 11
The proposed Deep-Pneumonia framework steps.
Figure 12
Figure 12
Accuracy performance metric.
Figure 13
Figure 13
Recall performance metric.
Figure 14
Figure 14
F1-score performance metric.
Figure 15
Figure 15
Precision performance metric.
Figure 16
Figure 16
Area Under the Curve (AUC) performance metric.
See this image and copyright information in PMC

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