Efficient Pneumonia Detection for Chest Radiography Using ResNet-Based SVM
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Jan 12, 2021
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Chest radiography has a significant clinical utility in the medical imaging diagnosis, as it is one of the most basic examination tools. Pneumonia is a common infection that rapidly affects human lung areas. So, finding an advanced automated method to detect Pneumonia is assigned to be one of the most recent issues, which is still prohibitively expensive to mass adoption, especially in the developing countries. This article presents an innovative approach for distinguishing the residence of pneumonia by embedding computational techniques to chest x-rays images which eliminating the demands for single-image investigation and significantly decrease the total costs. Recent advances in deep learning achieved remarkable results in image classification on different domains; however, its application for Pneumonia diagnosis is still restricted. Hence, the main focus is to provide an investigation that will improve the research in this area, presenting a new proposal to the applications of pre-trained convolutional neural networks (CNNs) as a stage of features extraction to detect this disease. Specifically, we propose to combine deep residual neural networks (ResNets), which extract the hierarchical features from the individual x-ray images with the boosting algorithm to select the salient features, and support vector machine for classification (AdaBoost-SVM). After conducting the performance analysis on the available dataset, we have concluded that the precision of the introduced scheme in Pneumonia classification is superior to the most concurrent approaches, resulting in a great improvement in clinical outcomes.
Keywords: Deep-learning; convolutional Neural Network; ResNet; AdaBoost; SVM; Pneumonia
References
Lakhani, P., & Sundaram, B. “Deep Learning at Chest Radiography: Automated Classification of Pulmonary Tuberculosis by Using Convolutional Neural Networks ”. Radiology,284(2),pp.574–582. 2017.
Sharma, Raju , A., D., & Ranjan, S. “Detection of pneumonia clouds in chest x-ray using image processing approach”. International Conference on Engineering, Nirma University ,NUiCONE 2017.
Lakhani, P. “ Deep Convolutional Neural Networks for Endotracheal Tube Position and x-ray Image Classification : Challenges and Opportunities ”. Digital Imaging Journal, 30(4), pp.460–468. 2017.
City, Q., & Hutchison, D. “Deep Learning in Medical and Multimodal Learning”, (Vol. 3).2017.
Stephen, O., Maduh, Sain, M., U. J., & Jeong, D. U. “An Efficient Deep Learning Approach to Pneumonia Classification in Healthcare”. healthcare engineering Journal, 2019.
Esteva, A., Novoa, Kuprel, B., R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. “ Dermatologist-level classification of skin cancer with deep neural networks”. Nature, 542(7639), 115. 2017.
Grewal, M., Srivastava, M. M., Kumar, P., & Varadarajan, S. Radnet: “Radiologist level accuracy using deep learning for hemorrhage detection in ct scans”. IEEE International Symposium on Biomedical Imaging (ISBI).pp. 281-284.April 2018.
Rajpurkar, P., A. Y., Hannun, Haghpanahi, M., Bourn, C., & Ng, A. Y. “Cardiologist-level arrhythmia detection with convolutional neural networks”. arxiv :1707.01836., 2017.
Ibrahim, A., El-kenawy, E. S. M. . Image Segmentation Methods Based on Superpixel Techniques: A Survey. Journal of Computer Science and Information Systems,15 (3 October 2020). (2020).
Ibrahim, A., El-kenawy, E. S. M. . Applications and Datasets for Superpixel Techniques: A Survey. Journal of Computer Scienceand Information Systems,15 (3 October 2020). (2020).
Lakhani, P., & Sundaram, B. “ Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks ”. Radiology, 284(2), pp.574-582. 2017.
El-Kenawy, El-Sayed M., Abdelhameed Ibrahim, Seyedali Mirjalili, Marwa Metwally Eid, and Sherif E. Hussein. "Novel feature selection and voting classifier algorithms for COVID-19 classification in CT images." IEEE Access 8 (2020): 179317-179335
.Islam, M. T., Aowal, M. A., Minhaz, A. T., & Ashraf, K. “Abnormality detection and localization in chest x-rays using deep convolutional neural networks”. arxiv :1705.09850. 2017.
Yao, L., Poblenz, E., Dagunts, D., Covington, B., Bernard, D., & Lyman, K. “ Learning to diagnose from scratch by exploiting dependencies among labels ”. arxiv :1710.10501.2017.
Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., & Summers, R. M. “Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases”. IEEE conference on computer vision and pattern recognition., pp. 2097-2106. 2017.
Shin, H. C., Lu, L., Kim, L., Seff, A., Yao, J., & Summers, R. M. “Interleaved Text/Image Deep Mining on a Large-Scale Radiology Database for Automated Image Interpretation”. Journal of Machine Learning Research, 17.pp.1-31, 2. 2016.
Fouad, Mohamad M., Ali Ibrahim El-Desouky, Rami Al-Hajj, and El-Sayed M. El-Kenawy. "Dynamic group-based cooperative optimization algorithm." IEEE Access 8 (2020): 148378-148403.
Melendez, J., van Ginneken, B., Maduskar, P., Philipsen, R. H., Reither, K., Breuninger, M., & Sánchez, C. I. “A novel multiple-instance learning-based approach to computer-aided detection of tuberculosis on chest x-rays”. IEEE transactions on medical imaging, 34(1), pp.179-192. 2014.
Jaeger, S., Karargyris, A., Candemir, S., Folio, L., Siegelman, J., Callaghan, F., & Thoma, G. “ Automatic tuberculosis screening using chest radiographs ”. IEEE transactions on medical imaging, 33(2), pp.233-245. 2013.
Xue, Z., You, D., Candemir, S., Jaeger, S., Antani, S., Long, L. R., & Thoma, G. R. “ Chest x-ray image view classification”. IEEE International Symposium on Computer-Based Medical Systems .pp. 66-71. June ,2015.
Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., ... & Sánchez, C. I.” A survey on deep learning in medical image analysis”. Medical image analysis, 42, pp.60-88. 2017.
Hassib, Eslam M., Ali I. El-Desouky, Labib M. Labib, and El-Sayed M. El-kenawy. "WOA+ BRNN: An imbalanced big data classification framework using Whale optimization and deep neural network." soft computing 24, no. 8 (2020): 5573-5592.
https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia,
Han, J., Zhang, D., Cheng, G., Liu, N., & Xu, D. “ Advanced deep-learning techniques for salient and category-specific object detection: a survey ”. IEEE Signal Processing Magazine, 35(1), pp.84-100. 2018.
El-kenawy, E. S. M. T. "A Machine Learning Model for Hemoglobin Estimation and Anemia Classification." International Journal of Computer Science and Information Security (IJCSIS) 17, no. 2 (2019).
Simonyan, K., & Zisserman, A. “ Very deep convolutional networks for large-scale image recognition ”. arxiv arxiv:1409.1556. 2014.
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., & Rabinovich, A. “Going deeper with convolutions”. IEEE conference on computer vision and pattern recognition .pp. 1-9., 2015.
He, K., Zhang, X., Ren, S., & Sun, J. “Deep residual learning for image recognition”. IEEE conference on computer vision and pattern recognition, pp. 770-778. 2016.
Wang, R.” AdaBoost for feature selection, classification and its relation with SVM, a review”. Physics Procedia, 25, pp.800-807. 2012.
Jung, H., Choi, M. K., Jung, J., Lee, J. H., Kwon, S., & Jung, W. Y. “ResNet-Based Vehicle Classification and Localization in Traffic Surveillance Systems”. IEEE Conference on Computer Vision and Pattern Recognition Workshops. pp. 934–940. July ,2017.
Zagoruyko, S., & Komodakis, N. “Wide Residual Networks”. Retrieved from arxiv.org, 2016.
Mahmood, A., M., Bennamoun, An, S., & Sohel, F. “Resfeats: Residual network based features for image classification”. International Conference on Image Processing, ICIP. pp.1597–1601. 2018.
He, K.; Zhang, X.; Ren, S.; Sun, J. “Deep residual learning for image recognition”. IEEE Conference on Computer Vision and Pattern Reconition, Las Vegas, NV, USA, pp. 770–778. June, 2016.
Lecun, Y.; Bottou, Bengio, L.; Y.; Haffner, P.” Gradient-based learning applied to document recognition”. Proc. IEEE. 86, pp.2278–2324. 1998.
Ioffe, S.; Szegedy, C. “Batch normalization: Accelerating deep network training by reducing internal covariate shift”. In Proceedings of the International Conference on Machine Learning, Lille, France, pp. 448–456. July, 2015.
E.-S. El-Kenawy and M. Eid, “Hybrid gray wolf and particle swarm optimization for feature selection,” International Journal of Innovative Computing Information and Control, vol. 16, no. 3, pp. 831–844, 2020.
Glorot, X.; Bordes, A.; Bengio, Y. “Deep sparse rectifier neural networks”. 14th International Conference on Artificial Intelligence and Statistics, Fort Lauderdale, FL, USA. pp. 315–323. April, 2011.
El-Kenawy, El-Sayed M., Marwa Metwally Eid, Mohamed Saber, and Abdelhameed Ibrahim. "MbGWO-SFS: Modified binary grey wolf optimizer based on stochastic fractal search for feature selection." IEEE Access 8 (2020): 107635-107649.
Jiao, Y., & Du, P. “Performance measures in evaluating machine learning based bioinformatics predictors for classifications”. Quantitative Biology, 4(4), pp.320–330. 2016.
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[1]
Eid, M.M. and Elawady, Y.H. 2021. Efficient Pneumonia Detection for Chest Radiography Using ResNet-Based SVM. European Journal of Electrical Engineering and Computer Science. 5, 1 (Jan. 2021), 1-8. DOI:https://doi.org/10.24018/ejece.2021.5.1.268.
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