Deep Convolutional Neural Networks Approach for Classification of Lung Diseases using X-Rays: COVID-19, Pneumonia, and Tuberculosis

doi:10.23940/ijpe.20.09.p2.13321340

Abstract

Abstract: In this study, we have proposed a model to identify pulmonary diseases such as COVID-19, pneumonia, and tuberculosis from X-ray images. Identifying and differentiating among these diseases is already a hard task for doctors. Recent findings obtained using radiology imaging techniques suggest that X-ray images contain salient information about these diseases. Application of deep convolutional neural networks coupled with radiological imaging can be beneficial for the accurate diagnosis of these diseases, and it can also be assistive to overcome the problem of shortage of healthcare experts in remote villages. We have implemented eight deep convolutional neural network (Deep CNN) models: AlexNet, VGG16, VGG19, DenseNet201, Xception, ResNet50, Sequential, and InceptionV3. Comparative analysis of the implemented models suggests that deep learning with X-ray imaging extracts significant biomarkers related to these diseases, while the best accuracy and least loss is obtained while training our model with VGG16. The training accuracy, precision, and false-positive obtained for VGG16 is 99.66%, 97.56%, and 2.24%, respectively.

Key words: deep CNN, feature extraction, multiclass classification, healthcare

Narayani Patil, Kalyani Ingole, and T. Rajani Mangala. Deep Convolutional Neural Networks Approach for Classification of Lung Diseases using X-Rays: COVID-19, Pneumonia, and Tuberculosis [J]. Int J Performability Eng, 2020, 16(9): 1332-1340.

Add to citation manager EndNote|Reference Manager|ProCite|BibTeX|RefWorks

References

1. A. Ramesh, C. Kambhampati, J. Monson,P. Drew, “Artificial Intelligence in Medicine,” Annals of the Royal College of Surgeons of England, Vol. 86, No. 5, pp. 334-338, September 2004
2. B. Dadonaite, “Pneumonia - Our World in Data,” (https://ourworldindata.org/pneumonia, accessed June 2020)
3. “Global Burden of Disease Collaborative Network, Global Burden of Disease Study, Results,” Institute for Health Metrics and Evaluation (IHME), Seattle, United States, (http://ghdx.healthdata.org/gbd-results-tool , accessed June 2020)
4. M. Roser, H. Ritchie, E. Ortiz-Ospina,J. Hasell,“Coronavirus Pandemic (COVID-19) - Statistics and Research,” - Our World in Data, 2020 (https://ourworldindata.org/coronavirus, accessed June 2020)
5. T. Ozturk, M. Talo, E. A. Yildirim, U. B. Baloglu, O. Yildirim,U. Rajendra Acharya, “Automated Detection of COVID-19 Cases using Deep Neural Networks with X-Ray Images,” Computers in Biology and Medicine, Vol. 121, No. 103792, June 2020
6. I. D.Apostolopoulos and T. A. Mpesiana, “Covid-19: Automatic Detection from X-Ray Images Utilizing Transfer Learning with Convolutional Neural Networks,” Physical and Engineering Sciences in Medicine, Vol. 43, No. 2, pp. 635-640, 2020
7. P. K. Sethy, S. K. Behera, P. K. Ratha,P. Biswas, “Detection of Coronavirus Disease (COVID-19) based on Deep Features and Support Vector Machine,” International Journal of Mathematical, Engineering and Management Sciences, Vol. 5, No. 4, pp. 643-651, 2020
8. O. Stephen, M. Sain, U. J. Maduh,D. U. Jeong, “An Efficient Deep Learning Approach to Pneumonia Classification in Healthcare,”Journal of Healthcare Engineering, pp. 1-7, 2019
9. C. J. Saul, D. Y. Urey,C. D. Taktakoglu, “Early Diagnosis of Pneumonia with Deep Learning,”(http://arxiv.org/abs/1904.00937, accessed June 2020)
10. K. Kadam, S. Ahirrao, H. Kaur, S. Phansalkar,A. Pawar, “Deep Learning Approach For Prediction of Pneumonia,” International Journal of Scientific and Technology Research, Vol. 8, No. 10, 2019
11. F. Pasa, V. Golkov, F. Pfeiffer, D. Cremers,D. Pfeiffer, “Efficient Deep Network Architectures for Fast Chest X-Ray Tuberculosis Screening and Visualization,” Scientific Reports, Vol. 9, No. 1, pp. 6268, 2019
12. P. Lakhani and B. Sundaram, “Deep Learning at Chest Radiography: Automated Classification of Pulmonary Tuberculosis by using Convolutional Neural Networks,” Radiology, Vol. 284, No. 2, pp. 574-582, 2017
13. S. Jaeger, S. Candemir, S. Antani, Y. X. J.Wáng, P. X. Lu, and G. Thoma, “Two Public Chest X-Ray Datasets for Computer-Aided Screening of Pulmonary Diseases,” Quantitative Imaging in Medicine and Surgery, Vol. 4, No. 6, pp. 475-477, 2014
14. D. Kermany, K. Zhang,M. Goldbaum, “Mendeley Data - Labeled Optical Coherence Tomography (OCT) and Chest X-Ray Images for Classification,” (https://data.mendeley.com/datasets/rscbjbr9sj/2#file-41d542e7-7f91-47f6-9ff2-, accesses June 2020)
15. J. P. Cohen, P. Morrison, L. Dao, K. Roth, T. Q. Duong,M. Ghassemi, “COVID-19 Image Data Collection: Prospective Predictions Are the Future,” (http://arxiv.org/abs/2006.11988, accessed June 2020)
16. J. P. Cohen, P. Bertin,V. Frappier, “Chester: A Web Delivered Locally Computed Chest X-Ray Disease Prediction System,” (http://arxiv.org/abs/1901.11210, accessed June 2020)

[1]	P. Antony Seba and J. V. Bibal Benifa. Hybrid Outlier Detection Strategy and Weighted Decision Matrix Ordinal Classifier for CKD Severity Prediction [J]. Int J Performability Eng, 2023, 19(2): 144-154.
[2]	Sagnik Pal, Rutvik Patel, Vijayasherly V., and Ramani Selvanambi. Hashtag Recommendation System for Instagram Posts using Transfer Learning with EfficientNet and ALS Model [J]. Int J Performability Eng, 2022, 18(8): 552-558.
[3]	Roop Preet Kaur, Anshu Sharma, Inderpal Singh, and Rahul Malhotra. Deep Learning-Based Pneumonia Recognition from Chest X-Ray Images [J]. Int J Performability Eng, 2022, 18(5): 380-386.
[4]	Palak Girdhar, Prashant Johri, and Deepali Virmani. An Improved Empirical Hyper-Parameter Tuned Supervised Model for Human Activity Recognition based on Motion Flow and Deep Learning [J]. Int J Performability Eng, 2022, 18(11): 808-816.
[5]	D. Bhavana, K. Kishore Kumar, Medasani Bipin Chandra, P.V. Sai Krishna Bhargav, D. Joy Sanjanaa, and G. Mohan Gopi. Hand Sign Recognition using CNN [J]. Int J Performability Eng, 2021, 17(3): 314-321.
[6]	Bo Dan, Shan Gao, and Zhihong Ji. Ship Target Recognition Technology of Radar High Resolution Range Profile based on Machine Learning [J]. Int J Performability Eng, 2020, 16(4): 537-548.
[7]	Shuang Liu, Xing Cui, Jiayi Li, Hui Yang, and Niko Lukač. Pedestrian Detection based on Faster R-CNN [J]. Int J Performability Eng, 2019, 15(7): 1792-1801.
[8]	Xin Zhang, Jianmin Zhao, Xianglong Ni, Haiping Li, and Fucheng Sun. Degradation Index Extraction and Degradation Trend Prediction for Rolling Bearing [J]. Int J Performability Eng, 2019, 15(5): 1334-1342.
[9]	Xinliang Wang, Zhigang Guo, Jianlin Chen, Na Liu, and Wei Fang. Detection Algorithm of Friction and Wear State of Large Mechanical and Electrical Equipment in Coal Mine based on C-SVC [J]. Int J Performability Eng, 2019, 15(3): 813-821.
[10]	Yanan Liu and Xinghao Guo. Modulation Recognition based on Wavelet Transform and Fractal Theory [J]. Int J Performability Eng, 2019, 15(3): 998-1004.
[11]	Yanhua Wang, Yaqiu Liu, and Weipeng Jing. Hadoop-based Parallel Algorithm for Data Mining in Remote Sensing Images [J]. Int J Performability Eng, 2019, 15(11): 2860-2870.
[12]	Cheng Peng, Jing He, Hao Chi, Xinpan Yuan, and Xiaojun Deng. Icing Prediction of Fan Blade based on a Hybrid Model [J]. Int J Performability Eng, 2019, 15(11): 2882-2890.
[13]	Wei Huang, Xiao Dong, Wenqian Shang, Weiguo Lin, and Menghan Yan. An Improved Optimal Method for Classification Problem [J]. Int J Performability Eng, 2019, 15(11): 3031-3041.
[14]	Shaohui Zhang, Man Wang, Canyi Du, and Edgar Estupinan. Local and Global SR for Bearing Sensor-based Vibration Signal Classification [J]. Int J Performability Eng, 2019, 15(10): 2657-2666.
[15]	Shaohua Yang, Guoliang Lu, Aiqun Wang, and Peng Yan. High-Level Feature Extraction based on Correlogram for State Monitoring of Rotating Machinery with Vibration Signals [J]. Int J Performability Eng, 2019, 15(1): 220-229.