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2023 | OriginalPaper | Buchkapitel

Medical X-ray Image Classification Method Based on Convolutional Neural Networks

verfasst von : Veska Gancheva, Tsviatko Jongov, Ivaylo Georgiev

Erschienen in: Bioinformatics and Biomedical Engineering

Verlag: Springer Nature Switzerland

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Abstract

Artificial intelligence and machine learning, including convolutional neural networks are increasingly entering the field of healthcare and medicine. The aim of the study is to optimize the learning process of convolutional neural networks through X-ray images pre-processing. A model for optimizing the overall architecture of a classifying convolutional neural network of chest X-rays by reducing the total number of convolutional operations is presented. The experimental results prove the successful application of the optimization process on the training of classification convolutional networks. There is a significant reduction in the training time of each epoch in the optimized convolutional networks. The optimization is of the order of 25% for the network with an input layer size of 124 × 124 and about 27% for the network with an input layer size of 122 × 122. The method can be applied in any field of image classification in which the informative image regions are grouped and subject to segmentation.

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Vorheriges Kapitel Predicting Papillary Renal Cell Carcinoma Prognosis Using Integrative Analysis of Histopathological Images and Genomic Data

Nächstes Kapitel Digital Breast Tomosynthesis Reconstruction Techniques in Healthcare Systems: A Review

Bohr, A., Memarzadeh, K.: The rise of artificial intelligence in healthcare applications. Artificial Intelligence in Healthcare, 25–60 (2020). https://doi.org/10.1016/B978-0-12-818438-7.00002-2. Epub 2020 Jun 26. PMCID: PMC7325854

Habehh, H., Gohel, S.: Machine learning in healthcare. Curr. Genomics 22(4), 291–300 (2021). https://doi.org/10.2174/1389202922666210705124359. PMID: 35273459; PMCID: PMC8822225

Javaid, M., et al.: Significance of machine learning in healthcare: features, pillars and applications. Int. J. Intell. Netw. 3, 58–73 (2022). ISSN 2666–6030. https://doi.org/10.1016/j.ijin.2022.05.002

Soffer, S., Ben-Cohen, A., Shimon, O., Amitai, M.M., Greenspan, H., Klang, E.: Convolutional neural networks for radiologic images: a radiologist’s guide. Radiology 290(3), 590–606 (2019). https://doi.org/10.1148/radiol.2018180547CrossRefPubMed

Borkowski, A.A., Viswanadhan, N.A., Thomas, L.B., Guzman, R.D., Deland, L.A., Masto-rides, S.M.: Using artificial intelligence for COVID-19 chest x-ray diagnosis. Federal Practitioner: for the health care professionals of the VA, DoD, and PHS 37(9), 398–404 (2020). https://doi.org/10.12788/fp.0045

Reshi, A.A., et al.: An efficient CNN model for COVID-19 disease detection based on x-ray image classification. Complexity 2021, Article ID 6621607, 12 (2021). https://doi.org/10.1155/2021/6621607

Wang, S., et al.: A deep learning algorithm using CT images to screen for Corona virus disease (COVID-19). Eur. Radiol. 31(8), 6096–6104 (2021). https://doi.org/10.1007/s00330-021-07715-1CrossRefPubMedPubMedCentral

Arias-Garzón, D., et al.: COVID-19 detection in x-ray images using convolutional neural networks. Mach. Learn. Appl. 6, 100138 (2021). ISSN 2666–8270.https://doi.org/10.1016/j.mlwa.2021.100138

Sorić, M., Pongrac, D., Inza, I.: Using convolutional neural network for chest x-ray image classification. In: 43rd International Convention on Information, Communication and Electronic Technology (MIPRO) (2020). https://doi.org/10.23919/MIPRO48935.2020.9245376

10.

Nkwentsha, X., Nkwentsha, X., Hounkanrin, A., Hounkanrin, A., Nicolls, F.: Automatic classification of medical x-ray images with convolutional neural networks. In: 2020 International SAUPEC/RobMech/PRASA Conference. https://doi.org/10.1109/SAUPEC/RobMech/PRASA48453.2020.9041052

11.

Yang, N., Niu, H., Chen, L., et al.: X-ray weld image classification using improved convolutional neural network. AIP Conference Proceedings 1995, 020035 (2018). https://doi.org/10.1063/1.5048766

12.

Yadav, S.S., Jadhav, S.M.: Deep convolutional neural network based medical image classification for disease diagnosis. J. Big Data 6(1), 1–18 (2019). https://doi.org/10.1186/s40537-019-0276-2CrossRef

13.

Shah, A., Shah, M.: Advancement of deep learning in pneumonia/covid-19 classification and localization: a systematic review with qualitative and quantitative analysis. J. Chronic Dis. Transl. Med. 8, 154–171 (2022). https://doi.org/10.1002/cdt3.17CrossRef

14.

Baltazar, L.R., et al.: Artificial intelligence on COVID-19 pneumonia detection using chest x-ray images (2021). https://doi.org/10.1371/journal.pone.0257884

15.

Bhatele, K.R., Jha, A., Tiwari, D., et al.: COVID-19 detection: a systematic review of machine and deep learning-based approaches utilizing chest x-rays and CT scans. Cogn. Comput. (2022). https://doi.org/10.1007/s12559-022-10076-6

16.

Luján-García, J.E., Moreno-Ibarra, M.A., Villuendas-Rey, Y., Yáñez-Márquez, C.: Fast COVID-19 and pneumonia classification using chest x-ray images. Mathematics 8, 1423 (2020). https://doi.org/10.3390/math8091423CrossRef

17.

Chest Xray Masks and Labels, Pulmonary Chest X-Ray Defect Detection. https://www.kaggle.com/nikhilpandey360/chest-xray-masks-and-labels

18.

Jaeger, S., et al.: Automatic tuberculosis screening using chest radiographs. IEEE Trans. Med. Imaging 33(2), 233–245 (2014). https://doi.org/10.1109/TMI.2013.2284099. PMID: 24108713

19.

Candemir, S., et al.: Lung segmentation in chest radiographs using anatomical atlases with nonrigid registration. IEEE Trans. Med. Imaging 33(2), 577–590 (2014). https://doi.org/10.1109/TMI.2013.2290491. PMID: 24239990CrossRefPubMed

20.

Jaegerm, S., Candemirm, S., Antanim, S., Wángm, Y.X., Lum, P.X., Thomam, G.: Two public chest x-ray datasets for computer-aided screening of pulmonary diseases. Quant. Imaging Med. Surg. 4(6), 475–477 (2014). https://doi.org/10.3978/j.issn.2223-4292.2014.11.20. PMID: 25525580; PMCID: PMC4256233

21.

Brownlee, J.A.: Gentle Introduction to the Rectified Linear Unit (ReLU). Machine Learning Mastery (2019)

22.

Chest X-Ray Images (Pneumonia). https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia

23.

Kermany, D., et al.: Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell 172(5), P1122-1131 (2018). https://doi.org/10.1016/j.cell.2018.02.010CrossRef

Titel: Medical X-ray Image Classification Method Based on Convolutional Neural Networks
verfasst von: Veska Gancheva
Tsviatko Jongov
Ivaylo Georgiev
Verlag: Springer Nature Switzerland
Buch: Bioinformatics and Biomedical Engineering
Print ISBN: 978-3-031-34959-1

Electronic ISBN: 978-3-031-34960-7

Copyright-Jahr: 2023
DOI: https://doi.org/10.1007/978-3-031-34960-7_16

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