Application of Lung Diseases Detection based on CSLNet
DOI:
https://doi.org/10.23887/janapati.v12i3.68815Keywords:
Lung Diseases, CSLNet, CNN, LSTMAbstract
Lung diseases caused by fungal or bacterial infections can lead to inflammation in lung and even death when not detected early. A standard method for diagnosing lung diseases is the use of chest X-ray, which require careful examination of X-ray images by a radiology expert. Therefore, this study proposes several new architecture models, namely CSLNet, to classify chest X-ray images for diagnosing whether patients suffer from COVID-19, viral pneumonia, bacterial pneumonia, tuberculosis, and normal. The experimental results show that the model has an 0.99 average Accuracy, 0.98 Precision, 0.98 Recall, and 0.98 f1-score. Meanwhile, the Receiver Operating Characteristic (ROC) for bacterial pneumonia, COVID-19, normal, tuberculosis, and viral pneumonia are 0.97, 0.99, 0.99, 0.94, and 0.97 respectively. This study is based on a deep learning with a new model, CSLNet, which can work well on the dataset of chest X-ray images used for diagnosing lung diseases.
References
S. Safiri et al., “Burden of chronic obstructive pulmonary disease and its attributable risk factors in 204 countries and territories, 1990-2019: Results from the Global Burden of Disease Study 2019,” BMJ, 2022, doi: 10.1136/bmj-2021-069679.
F. Hussein et al., “Hybrid CLAHE-CNN Deep Neural Networks for Classifying Lung Diseases from X-ray Acquisitions,” Electronics, vol. 11, no. 19, p. 3075, 2022, doi: 10.3390/electronics11193075.
S. Bharati, P. Podder, and M. R. H. Mondal, “Hybrid deep learning for detecting lung diseases from X-ray images,” Informatics Med. Unlocked, vol. 20, p. 100391, 2020, doi: 10.1016/j.imu.2020.100391.
M. Oloko-Oba and S. Viriri, Diagnosing tuberculosis using deep convolutional neural network, vol. 12119 LNCS. Springer International Publishing, 2020. doi: 10.1007/978-3-030-51935-3_16.
World Health Organization, “Laboratory testing for coronavirus disease 2019 (COVID-19) in suspected human cases,” vol. 2019, no. March, 2020.
S. M. Fati, E. M. Senan, and N. ElHakim, “Deep and Hybrid Learning Technique for Early Detection of Tuberculosis Based on X-ray Images Using Feature Fusion,” Appl. Sci., vol. 12, no. 14, p. 7092, 2022, doi: 10.3390/app12147092.
M. Z. Alom, M. M. S. Rahman, M. S. Nasrin, T. M. Taha, and V. K. Asari, “COVID_MTNet: COVID-19 Detection with Multi-Task Deep Learning Approaches,” 2020, [Online]. Available: http://arxiv.org/abs/2004.03747
M. F. Aslan, “A robust semantic lung segmentation study for CNN-based COVID-19 diagnosis,” Chemom. Intell. Lab. Syst., vol. 231, no. October, p. 104695, 2022, doi: 10.1016/j.chemolab.2022.104695.
M. Heidari, S. Mirniaharikandehei, A. Z. Khuzani, G. Danala, Y. Qiu, and B. Zheng, “Improving the performance of CNN to predict the likelihood of COVID-19 using chest X-ray images with preprocessing algorithms,” Int. J. Med. Inform., vol. 144, no. September, p. 104284, 2020, doi: 10.1016/j.ijmedinf.2020.104284.
R. Karthik, R. Menaka, and M. Hariharan, “Learning distinctive filters for COVID-19 detection from chest X-ray using shuffled residual CNN,” Appl. Soft Comput., vol. 99, p. 106744, 2021, doi: 10.1016/j.asoc.2020.106744.
S. Thakur and A. Kumar, “X-ray and CT-scan-based automated detection and classification of covid-19 using convolutional neural networks (CNN),” Biomed. Signal Process. Control, vol. 69, no. March, p. 102920, 2021, doi: 10.1016/j.bspc.2021.102920.
A. Narula and N. K. Vaegae, “Development of CNN-LSTM combinational architecture for COVID-19 detection,” J. Ambient Intell. Humaniz. Comput., no. 0123456789, 2022, doi: 10.1007/s12652-022-04508-2.
A. Irfan, A. L. Adivishnu, A. Sze-To, T. Dehkharghanian, S. Rahnamayan, and H. R. Tizhoosh, “Classifying Pneumonia among Chest X-Rays Using Transfer Learning,” Proc. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. EMBS, vol. 2020-July, pp. 2186–2189, 2020, doi: 10.1109/EMBC44109.2020.9175594.
F. Ucar and D. Korkmaz, “COVIDiagnosis-Net: Deep Bayes-SqueezeNet based diagnosis of the coronavirus disease 2019 (COVID-19) from X-ray images,” Med. Hypotheses, vol. 140, no. April, p. 109761, 2020, doi: 10.1016/j.mehy.2020.109761.
D. S. Kermany et al., “Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning,” Cell, vol. 172, no. 5, pp. 1122-1131.e9, 2018, doi: 10.1016/j.cell.2018.02.010.
D. Varshni, K. Thakral, L. Agarwal, R. Nijhawan, and A. Mittal, “Pneumonia Detection Using CNN based Feature Extraction,” Proc. 2019 3rd IEEE Int. Conf. Electr. Comput. Commun. Technol. ICECCT 2019, 2019, doi: 10.1109/ICECCT.2019.8869364.
Ü. Budak, Z. Cömert, M. Çıbuk, and A. Şengür, “DCCMED-Net: Densely connected and concatenated multi Encoder-Decoder CNNs for retinal vessel extraction from fundus images,” Med. Hypotheses, vol. 134, no. August 2019, 2020, doi: 10.1016/j.mehy.2019.109426.
F. Özyurt, E. Sert, and D. Avcı, “An expert system for brain tumor detection: Fuzzy C-means with super resolution and convolutional neural network with extreme learning machine,” Med. Hypotheses, vol. 134, no. September 2019, 2020, doi: 10.1016/j.mehy.2019.109433.
F. N. Iandola, S. Han, M. W. Moskewicz, K. Ashraf, W. J. Dally, and K. Keutzer, “SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size,” pp. 1–13, 2016, [Online]. Available: http://arxiv.org/abs/1602.07360
G. Fan, F. Chen, D. Chen, and Y. Dong, “Recognizing multiple types of rocks quickly and accurately based on lightweight CNNs model,” IEEE Access, vol. 8, pp. 55269–55278, 2020, doi: 10.1109/ACCESS.2020.2982017.
R. T. N. V. S. Chappa and M. El-Sharkawy, “Squeeze-and-Excitation SqueezeNext: An Efficient DNN for Hardware Deployment,” 2020 10th Annu. Comput. Commun. Work. Conf. CCWC 2020, pp. 691–697, 2020, doi: 10.1109/CCWC47524.2020.9031119.
L. Wang, Z. Q. Lin, and A. Wong, “COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images,” Sci. Rep., vol. 10, no. 1, pp. 1–12, 2020, doi: 10.1038/s41598-020-76550-z.
I. D. Apostolopoulos and T. A. Mpesiana, “Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks,” Phys. Eng. Sci. Med., vol. 43, no. 2, pp. 635–640, 2020, doi: 10.1007/s13246-020-00865-4.
A. Narin, C. Kaya, and Z. Pamuk, “Department of Biomedical Engineering, Zonguldak Bulent Ecevit University, 67100, Zonguldak, Turkey.,” arXiv Prepr. arXiv2003.10849., 2020, [Online]. Available: https://arxiv.org/abs/2003.10849
Y. Pathak, P. K. Shukla, A. Tiwari, S. Stalin, and S. Singh, “Deep Transfer Learning Based Classification Model for COVID-19 Disease,” Irbm, vol. 43, no. 2, pp. 87–92, 2022, doi: 10.1016/j.irbm.2020.05.003.
Khandar, “Tuberculosis (TB) Chest X-ray Database.” [Online]. Available: https://www.kaggle.com/datasets/tawsifurrahman/tub
JtiptJ, “Chest X-Ray (Pneumonia,Covid-19,Tuberculosis).” [Online]. Available: https://www.kaggle.com/datasets/jtiptj/chest-xray-
J. Simonsen and O. S. Jensen, “Spatial Transformer Networks,” ACM Int. Conf. Proceeding Ser., vol. 2, pp. 45–48, 2016, doi: 10.1145/2948076.2948084.
N. M. Elshennawy and D. M. Ibrahim, “Deep-Pneumonia Framework Using Deep Learning Models Based on Chest X-Ray Images,” Diagnostics, vol. 10, no. 9, pp. 1–16, 2020, doi: 10.3390/diagnostics10090649.
G. Chen, “A Gentle Tutorial of Recurrent Neural Network with Error Backpropagation,” pp. 1–10, 2016, [Online]. Available: http://arxiv.org/abs/1610.02583
T. Szandała, “Review and comparison of commonly used activation functions for deep neural networks,” Stud. Comput. Intell., vol. 903, pp. 203–224, 2021, doi: 10.1007/978-981-15-5495-7_11.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 Panji Bintoro, Zulkifli Zulkifli, Fitriana Fitriana, Sukarni Sukarni, Abdullah Abdullah
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with Janapati agree to the following terms:- Authors retain copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (CC BY-SA 4.0) that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work. (See The Effect of Open Access)
