Hierarchical convolutional models for automatic pneumonia diagnosis based on X-ray images: new strategies in public health
Keywords:
Accuracy, convolutional neural network, diagnostic processAbstract
Background. In order to help physicians and radiologists in diagnosing pneumonia, deep learning and other artificial intelligence methods have been described in several researches to solve this task. The main objective of the present study is to build a stacked hierarchical model by combining several models in order to increase the procedure accuracy.
Methods. Firstly, the best convolutional network in terms of accuracy were evaluated and described. Later, a stacked hierarchical model was built by using the most relevant features extracted by the selected two models. Finally, over the stacked model with the best accuracy, a hierarchically dependent second stage model for inner-classification was built in order to detect both inflammation of the pulmonary alveolar space (lobar pneumonia) and interstitial tissue involvement (interstitial pneumonia).
Results. The study shows how the adopted staked model lead to a higher accuracy. Having a high accuracy on pneumonia detection and classification can be a paramount asset to treat patients in real health-care environments.
Conclusions. Despite some limits, our findings support the notion that deep learning methods can be used to simplify the diagnostic process and improve disease management.
References
1. Napoli C, Dente MG, Kärki T, Riccardo F, Rossi
P, Declich S; Network for the Control of CrossBorder Health Threats in the Mediterranean Basin and Black Sea. Screening for Infectious Diseases among Newly Arrived Migrants: Experiences and Practices in Non-EU Countries of the Mediterranean Basin and Black Sea. Int J Environ Res Public Health. 2015 Dec 8; 12(12): 15550-8. doi: 10.3390/ijerph121215002.
2. Napoli C, Riccardo F, Declich S, et al. An early warning system based on syndromic surveillance to detect potential health emergencies among migrants: results of a two-year experience in Italy. Int J Environ Res Public Health. 2014 Aug 20; 11(8): 8529-41. doi: 10.3390/ ijerph110808529.
3. Illari S I, Russo S, Avanzato R, Napoli C. A cloud-oriented architecture for the remote assessment and follow-up of hospitalized patients. In: SYSTEM 2020: 5th Symposium for Young Scientists in Technology, Engineering and Mathematics, May 20 2020. CEUR Workshop Proceedings 2020; 2694: 29-35.
4. Napoli C, Salcuni P, Pompa MG, Declich S, Rizzo C. Estimated imported infections of Chikungunya and Dengue in Italy, 2008 to 2011. J Travel Med. 2012 Sep-Oct; 19(5): 294-7. doi: 10.1111/j.1708-8305.2012.00640.x. Epub 2012 Aug 8.
5. Capalbo C, Aceti A, Simmaco M, et al. The Exponential Phase of the Covid-19 Pandemic in Central Italy: An Integrated Care Pathway. Int J Environ Res Public Health. 2020 May 27; 17(11): 3792. doi: 10.3390/ijerph17113792.
6. Roma P, Monaro M, Muzi L, et al. How to Improve Compliance with Protective Health Measures during the COVID-19 Outbreak: Testing a Moderated Mediation Model and Machine Learning Algorithms. Int J Environ Res Public Health. 2020 Oct 4; 17(19): 7252. doi: 10.3390/ ijerph17197252.
7. Capizzi G, Lo Sciuto G, Napoli C, Połap D, Woźniak M. Small lung nodules detection based on fuzzy-logic and probabilistic neural network with bioinspired reinforcement learning. IEEE Transactions on Fuzzy Systems 2020 June; 28(6): 1178-89. doi: 10.1109/TFUZZ.2019.2952831.
8. Shorfuzzaman M, Masud M, Alhumyani H,
Anand D, Singh A. Artificial Neural NetworkBased Deep Learning Model for COVID-19 Patient Detection Using X-Ray Chest Images. J Healthc Eng 2021 Jun 5; 2021: 5513679. doi: 10.1155/2021/5513679.
9. Chouhan V, Singh SK, Khamparia A, et al. A novel transfer learning based approach for pneumonia detection in chest x-ray images. Appl Sci. 2020; 10(2): 559. https://doi.org/10.3390/ app10020559.
10. Woźniak M, Połap D, Napoli C, Tramontana
E. Graphic object feature extraction system based on cuckoo search algorithm. Expert Systems with Applications 2016; 66: 20-31. doi: 10.1016/j.eswa.2016.08.068.
11. Oh Y, Park S, Ye JC. Deep learning COVID-19 features on CXR using limited training data sets. IEEE Trans Med Imaging 2020 Aug; 39(8): 2688-700. doi: 10.1109/TMI.2020.2993291. Epub 2020 May 8.
12. Jain R, Gupta M, Taneja S, Hemanth DJ. Deep learning-based detection and analysis of COVID-19 on chest x-ray images. Appl Intell. 2021; 51: 1690-700. Epub 2020 Oct 9. https:// doi.org/10.1007/s10489-020-01902-1.
13. Pereira R M, Bertolini D, Teixeira LO, Silla CN, Costa YMG. COVID-19 identification in chest x-ray images on flat and hierarchical classification scenarios. Comput Methods Programs Biomed. 2020 Oct; 194: 105532. doi: 10.1016/j.
cmpb.2020.105532. Epub 2020 May 8.
14. Toğaçar M, Ergen B, Cömert Z, Özyurt F. A Deep Feature Learning Model for Pneumonia Detection Applying a Combination of mRMR Feature Selection and Machine Learning Models. IRBM. 2020 Aug; 41(4): 212-22. https://doi. org/10.1016/j.irbm.2019.10.006.
15. Rahman T, Chowdhury MEH, Khandakar A, et al. Transfer learning with deep Convolutional Neural Network (CNN) for pneumonia detection using chest X-ray. Appl Sci. 2020; 10(9): 3233. https://doi.org/10.3390/app10093233.
16. Hammam AA, Elmousalami HH, Hassanien AE. Stacking Deep Learning for Early COVID-19 Vision Diagnosis. In: Hassanien AE, Dey N, Elghamrawy S, eds. Big Data Analytics and Artificial Intelligence Against COVID-19: Innovation Vision and Approach. Studies in Big Data. Cham: Springer, 2020: 297-307.
17. Kermany DS, Goldbaum M, Cai W, et al. Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning. Cell. 2018 Feb; 172(5): 1122-31. doi: 10.1016/j.cell. 2018.02.010.
18. Howard AG, Zhu M, Chen B, et al. Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv 2017; 1704.04861.
19. Huang G, Liu Z, van der Maaten L, Weinberger KQ. Densely Connected Convolutional Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, 21-26 July 2017. IEEE, 2017: 4700-8. doi: 10.1109/ CVPR.2017.243.
20. Ho TKK, Gwak J. Multiple feature integration for classification of thoracic disease in chest radiography. Appl Sci. 2019; 9(19): 4130. https:// doi.org/10.3390/app9194130.
21. Maguolo G, Nanni L. A critic evaluation of methods for COVID-19 automatic detection from X-ray images. Inf Fusion. 2021 Dec; 76: 1-7. doi: 10.1016/j.inffus.2021.04.008. Epub 2021 Apr 30.
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