CHEST X-RAY IMAGE CLASSIFICATION USING FASTER R-CNN
DOI:
https://doi.org/10.24191/mjoc.v4i1.6095Keywords:
Image Classification, Chest X-ray Analysis, CNN, faster R-CNN, Region Proposal NetworkAbstract
Chest x-ray image analysis is the common medical imaging exam needed to assess different pathologies. Having an automated solution for the analysis can contribute to minimizing the workloads, improve efficiency and reduce the potential of reading errors. Many methods have been proposed to address chest x-ray image classification and detection. However, the application of regional-based convolutional neural networks (CNN) is currently limited. Thus, we propose an approach to classify chest x-ray images into either one of two categories, pathological or normal based on Faster Regional-CNN model. This model utilizes Region Proposal Network (RPN) to generate region proposals and perform image classification. By applying this model, we can potentially achieve two key goals, high confidence in the classification and reducing the computation time. The results show the applied model achieved higher accuracy as compared to the medical representatives on the random chest x-ray images. The classification model is also reasonably effective in classifying between finding and normal chest x-ray image captured through a live webcam.
References
Abiyev, R. H., & Ma’aitah, M. K. S. (2018). Deep Convolutional Neural Networks for Chest Diseases
Detection. Journal of Healthcare Engineering, 2018, 1–11.
https://doi.org/10.1155/2018/4168538
Agarwal, N., Balasubramanian, V. N., & Jawahar, C. V. (2018). Improving multiclass classification by deep networks using DAGSVM and Triplet Loss. Pattern Recognition Letters, 112, 184–190.
https://doi.org/10.1016/J.PATREC.2018.06.034
Ajay Mittal, Hooda, R., & Sofat, S. (2017). Lung field segmentation in chest radiographs: a historical review, current status, and expectations from deep learning. IET Image Processing, 11(11), 937 952. Retrieved from http://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2016.0526
Bar, Y., Diamant, I., Wolf, L., Lieberman, S., Konen, E., & Greenspan, H. (2016). Chest pathology identification using deep feature selection with non-medical training. Computer Methods in
Biomechanics and Biomedical Engineering: Imaging & Visualization, 6(3), 259–263.
https://doi.org/10.1080/21681163.2016.1138324
Chunhui Gu, Lim, J. J., Arbelaez, P., & Malik, J. (2009). Recognition using regions. 2009 IEEE Conference on Computer Vision and Pattern Recognition, 1030–1037.
https://doi.org/10.1109/CVPR.2009.5206727
Doi, K. (2007). Computer-aided diagnosis in medical imaging: historical review, current status and future potential. Computerized Medical Imaging and Graphics : The Official Journal of the
Computerized Medical Imaging Society, 31(4–5), 198–211.
https://doi.org/10.1016/j.compmedimag.2007.02.002
Donahue, J., Jia, Y., Vinyals, O., Hoffman, J., Zhang, N., Tzeng, E., & Darrell, T. (2014). DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition. Proceedings of the 31st International Conference on Machine Learning, PMLR, 647–655. Retrieved from
http://proceedings.mlr.press/v32/donahue14.html
Girshick, R. (2015). Fast R-CNN. Retrieved from http://arxiv.org/abs/1504.08083
Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014). Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation. 2014 IEEE Conference on Computer Vision and Pattern Recognition, 580–587. https://doi.org/10.1109/CVPR.2014.81
Guan, Q., Huang, Y., Zhong, Z., Zheng, Z., Zheng, L., & Yang, Y. (2018). Diagnose like a Radiologist: Attention Guided Convolutional Neural Network for Thorax Disease Classification.
Retrieved from http://arxiv.org/abs/1801.09927
Gundel, S., Grbic, S., Georgescu, B., Zhou, K., Ritschl, L., Meier, A., & Comaniciu, D. (2018). Learning to recognize Abnormalities in Chest X-Rays with Location-Aware Dense Networks. Retrieved from http://arxiv.org/abs/1803.04565
Hollander, M., & Wolfe, D. A. (1999). Nonparametric statistical methods (2nd ed.). Wiley.
Islam, M. T., Aowal, M. A., Minhaz, A. T., & Ashraf, K. (2017). Abnormality Detection and Localization in Chest X-Rays using Deep Convolutional Neural Networks. Retrieved from http://arxiv.org/abs/1705.09850
Jamil, M. Z. A., Mutalib, S., rahman, S. A., & Aziz, Z. A. (2018). Classification of Paddy Weed Leaf using Neuro-Fuzzy Methods. MALAYSIAN JOURNAL OF COMPUTING, 3(1), 54–66.
Retrieved from http://gids.mohe.gov.my/index.php/mjoc/article/view/4883
Ker, J., Wang, L., Rao, J., & Lim, T. (2018). Deep Learning Applications in Medical Image Analysis.
IEEE Access, 6, 9375–9389. https://doi.org/10.1109/ACCESS.2017.2788044
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks. Proceedings of the 25th International Conference on Neural Information Processing Systems - Volume 1, pp. 1097–1105. Retrieved from
https://dl.acm.org/citation.cfm?id=2999257
Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., … Sánchez, C. I.
(2017). A survey on deep learning in medical image analysis. Medical Image Analysis, 42, 6088. https://doi.org/10.1016/j.media.2017.07.005
Rahmat, T., Ismail, A., & Aliman, S. (2018). Chest X-rays Image Classification in Medical Image Analysis. Applied Medical Informatics, 40(3–4), 63–73. Retrieved from
http://ami.info.umfcluj.ro/index.php/AMI/article/view/639
Rajpurkar, P., Irvin, J., Zhu, K., Yang, B., Mehta, H., Duan, T., … Ng, A. Y. (2017a). CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning. Retrieved from http://arxiv.org/abs/1711.05225
Rajpurkar, P., Irvin, J., Zhu, K., Yang, B., Mehta, H., Duan, T., … Ng, A. Y. (2017b). CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning. Retrieved from http://arxiv.org/abs/1711.05225
Ren, S., He, K., Girshick, R., & Sun, J. (2017). Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. IEEE Transactions on Pattern Analysis and Machine
Intelligence, 39(6), 1137–1149. https://doi.org/10.1109/TPAMI.2016.2577031
Sa, R., Owens, W., Wiegand, R., Studin, M., Capoferri, D., Barooha, K., … Chaudhary, V. (2017).
Intervertebral disc detection in X-ray images using faster R-CNN. 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC),
564–567. https://doi.org/10.1109/EMBC.2017.8036887 Sedai, S., Mahapatra, D., Ge, Z., Chakravorty, R., & Garnavi, R. (2018). Deep Multiscale Convolutional Feature Learning for Weakly Supervised Localization of Chest Pathologies in Xray Images. https://doi.org/10.1007/978-3-030-00919-9_31
Shin, H.-C., Roberts, K., Lu, L., Demner-Fushman, D., Yao, J., & Summers, R. M. (2016). Learning to Read Chest X-Rays: Recurrent Neural Cascade Model for Automated Image Annotation.
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2497–2506.
https://doi.org/10.1109/CVPR.2016.274
Tensorflow detection model zoo - COCO-trained models. (2017). Retrieved from https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md
Uijlings, J. R. R., van de Sande, K. E. A., Gevers, T., & Smeulders, A. W. M. (2013). Selective
Search for Object Recognition. International Journal of Computer Vision, 104(2), 154–171.
https://doi.org/10.1007/s11263-013-0620-5
Wang, L., Xie, X.-L., Bian, G.-B., Hou, Z.-G., Cheng, X.-R., & Prasong, P. (2017). Guide-wire
detection using region proposal network for X-ray image-guided navigation. 2017 International
Joint Conference on Neural Networks (IJCNN), 3169–3175.
https://doi.org/10.1109/IJCNN.2017.7966251
Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., & Summers, R. M. (2017a). ChestX-ray8: Hospital
scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and
Localization of Common Thorax Diseases. Retrieved from http://arxiv.org/abs/1705.02315
Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., & Summers, R. M. (2017b). ChestX-ray8: Hospital
scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and
Localization of Common Thorax Diseases. Retrieved from http://arxiv.org/abs/1705.02315
Yamashita, R., Nishio, M., Do, R. K. G., & Togashi, K. (2018). Convolutional neural networks: an
overview and application in radiology. Insights into Imaging, 9(4), 611–629.
https://doi.org/10.1007/s13244-018-0639-9
Yao, L., Poblenz, E., Dagunts, D., Covington, B., Bernard, D., & Lyman, K. (2017). Learning to
diagnose from scratch by exploiting dependencies among labels. Eprint ArXiv:1710.10501.
Retrieved from http://arxiv.org/abs/1710.10501
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