Detection of Pneumonia with a Novel CNN-based Approach

Ebru Erdem; Tolga Aydin

doi:10.35377/saucis.04.01.787030

Research Article

Year 2021, , 26 - 34, 30.04.2021

Ebru Erdem , Tolga Aydin

https://doi.org/10.35377/saucis.04.01.787030

Cited By: 9

Abstract

References

K. He, G. Gkioxari, P. Dollar, and R. Girshick, "Mask r-cnn," in Proceedings of the IEEE international conference on computer vision., 2017.
T.-Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollar, "Focal loss for dense object detection," in Proceedings of the IEEE international conference on computer vision., 2017.
I. Sirazitdinov, M. Kholiavchenko, T. Mustafaev, Y. Yixuan, R. Kuleev, and B. Ibragimov, "Deep neural network ensemble for pneumonia localization from a large-scale chest x-ray database," Computers & Electrical Engineering., vol. 78, pp. 388-399, 2019.
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, "Gradient-based learning applied to document recognition," Proceedings of the IEEE., vol. 86, no. 11, pp. 2278-2324, 1998.
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R. Siddiqi, "Automated Pneumonia Diagnosis using a Customized Sequential Convolutional Neural Network," in Proceedings of the 2019 3rd International Conference on Deep Learning Technologies., 2019.
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A. Mittal, D. Kumar, M. Mittal, T. Saba, I. Abunadi, A. Rehman, and S. Roy, "Detecting Pneumonia Using Convolutions and Dynamic Capsule Routing for Chest X-ray Images," Sensors., vol. 20, no. 4, pp. 1068, 2020.
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Detection of Pneumonia with a Novel CNN-based Approach

Year 2021, , 26 - 34, 30.04.2021

Ebru Erdem , Tolga Aydin

https://doi.org/10.35377/saucis.04.01.787030

Cited By: 9

Abstract

Pneumonia is a seasonal infectious lung tissue inflammatory disease. According to the World Health Organization (WHO), early diagnosis of the disease reduces the risk of its transmission and death. Various deep learning and machine learning algorithms were used for pneumonia detection. This study aims to analyze the lung images and diagnose pneumonia disease by employing deep learning approaches. We have suggested a novel deep learning framework for the detection of pneumonia in lung. A comparison was made between the proposed new deep learning model and pre-trained deep learning models. 88.62% accuracy rate has been obtained from the proposed deep learning structure. It was observed that by utilizing the new deep neural network developed, the accuracy results of VGG16 (88.78%) and VGG19 (88.30%), which are among the popular deep learning architectures, can be approximated. The test results show that our proposed model has a better recall value (97.43%) (VGG16 (93.33%) and VGG19 (96.92%)), and a better F1-Score (91.45%) (VGG16 (91.22%) and VGG19 (91.19%)).

Keywords

Pneumonia, VGG16, VGG19, CNN

References

K. He, G. Gkioxari, P. Dollar, and R. Girshick, "Mask r-cnn," in Proceedings of the IEEE international conference on computer vision., 2017.
T.-Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollar, "Focal loss for dense object detection," in Proceedings of the IEEE international conference on computer vision., 2017.
I. Sirazitdinov, M. Kholiavchenko, T. Mustafaev, Y. Yixuan, R. Kuleev, and B. Ibragimov, "Deep neural network ensemble for pneumonia localization from a large-scale chest x-ray database," Computers & Electrical Engineering., vol. 78, pp. 388-399, 2019.
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, "Gradient-based learning applied to document recognition," Proceedings of the IEEE., vol. 86, no. 11, pp. 2278-2324, 1998.
Y. LeCun and Y. Bengio, "Convolutional networks for images, speech, and time series," The handbook of brain theory and neural networks., vol. 3361, no. 10, pp. 1995, 1995.
K. Simonyan, and A. Zisserman, "Very deep convolutional networks for large-scale image recognition," arXiv preprint arXiv:1409., vol. 1556, 2014.
F. Chollet, "Xception: Deep learning with depthwise separable convolutions," in Proceedings of the IEEE conference on computer vision and pattern recognition., 2017.
E. Ayan, and H.M. Ünver, "Diagnosis of Pneumonia from Chest X-Ray Images Using Deep Learning," in 2019 Scientific Meeting on Electrical-Electronics & Biomedical Engineering and Computer Science (EBBT)., 2019.
A. Krizhevsky, I. Sutskever, and G.E. Hinton, "Imagenet classification with deep convolutional neural networks," in Advances in neural information processing systems., 2012.
K. He, X. Zhang, S. Ren, and J. Sun, "Deep residual learning for image recognition," in Proceedings of the IEEE conference on computer vision and pattern recognition., 2016.
A. Bhandary, G.A. Prabhu, V. Rajinikanth, K.P. Thanaraj, S.C. Satapathy, D.E. Robbins, C. Shasky, Y.-D. Zhang, J.M.R.S. Tavares, and N.S.M. Raja, "Deep-learning framework to detect lung abnormality–A study with chest X-Ray and lung CT scan images," Pattern Recognition Letters., vol. 129, pp. 271-278, 2020.
G. Liang and L. Zheng, " A transfer learning method with deep residual network for pediatric pneumonia diagnosis," Computer methods and programs in biomedicine., vol. 187, pp. 104964, 2020.
V. Chouhan, S.K. Singh, A. Khamparia, D. Gupta, P. Tiwari, C. Moreira, R. Damasevicius, and V.H.C. de Albuquerque, "A Novel Transfer Learning Based Approach for Pneumonia Detection in Chest X-ray Images," Applied Sciences., vol. 10, no. 2, pp. 559, 2020.
R. Siddiqi, "Automated Pneumonia Diagnosis using a Customized Sequential Convolutional Neural Network," in Proceedings of the 2019 3rd International Conference on Deep Learning Technologies., 2019.
S.S. Yadav, S.M. Jadhav, "Deep convolutional neural network based medical image classification for disease diagnosis," J Big Data., vol. 6, pp. 113, 2019.
K.E. Asnaoui, Y. Chawki, and A. Idri, "Automated methods for detection and classification pneumonia based on x-ray images using deep learning," arXiv preprint arXiv:2003., pp. 14363, 2020.
A. Mittal, D. Kumar, M. Mittal, T. Saba, I. Abunadi, A. Rehman, and S. Roy, "Detecting Pneumonia Using Convolutions and Dynamic Capsule Routing for Chest X-ray Images," Sensors., vol. 20, no. 4, pp. 1068, 2020.
R. Jain, P. Nagrath, G. Kataria, V.S. Kaushik, and D.J. Hemanth, "Pneumonia detection in chest X-ray images using convolutional neural networks and transfer learning," Measurement., vol. 165, pp. 108046, 2020.
S. Chakraborty, S. Aich, J.S. Sim, and H.C. Kim, "Detection of pneumonia from chest x-rays using a convolutional neural network architecture," In International Conference on Future Information & Communication Engineering., vol. 11, no. 1, pp. 98-102, 2019.
D. Kermany, K. Zhang, and M. Goldbaum, "Labeled optical coherence tomography (oct) and chest X-ray images for classification," Mendeley data., vol. 2, 2018.
P. Mooney, "Chest X-Ray Images (Pneumonia)," 2017. [Online]. Available: https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia. [Accessed: 27-June-2020].
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, Z. Wojna, "Rethinking the inception architecture for computer vision," in Proceedings of the IEEE conference on computer vision and pattern recognition., 2016.

There are 22 citations in total.

Details

Primary Language	English
Subjects	Artificial Intelligence
Journal Section	Articles
Authors	Ebru Erdem 0000-0002-4042-7549 Tolga Aydin 0000-0002-8971-3255
Publication Date	April 30, 2021
Submission Date	August 28, 2020
Acceptance Date	December 28, 2020
Published in Issue	Year 2021

Cite

IEEE	E. Erdem and T. Aydin, “Detection of Pneumonia with a Novel CNN-based Approach”, SAUCIS, vol. 4, no. 1, pp. 26–34, 2021, doi: 10.35377/saucis.04.01.787030.