{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T16:28:37Z","timestamp":1774024117393,"version":"3.50.1"},"reference-count":54,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2020,2,15]],"date-time":"2020-02-15T00:00:00Z","timestamp":1581724800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>An entity\u2019s existence in an image can be depicted by the activity instantiation vector from a group of neurons (called capsule). Recently, multi-layered capsules, called CapsNet, have proven to be state-of-the-art for image classification tasks. This research utilizes the prowess of this algorithm to detect pneumonia from chest X-ray (CXR) images. Here, an entity in the CXR image can help determine if the patient (whose CXR is used) is suffering from pneumonia or not. A simple model of capsules (also known as Simple CapsNet) has provided results comparable to best Deep Learning models that had been used earlier. Subsequently, a combination of convolutions and capsules is used to obtain two models that outperform all models previously proposed. These models\u2014Integration of convolutions with capsules (ICC) and Ensemble of convolutions with capsules (ECC)\u2014detect pneumonia with a test accuracy of 95.33% and 95.90%, respectively. The latter model is studied in detail to obtain a variant called EnCC, where n = 3, 4, 8, 16. Here, the E4CC model works optimally and gives test accuracy of 96.36%. All these models had been trained, validated, and tested on 5857 images from Mendeley.<\/jats:p>","DOI":"10.3390\/s20041068","type":"journal-article","created":{"date-parts":[[2020,2,20]],"date-time":"2020-02-20T03:20:03Z","timestamp":1582168803000},"page":"1068","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":116,"title":["Detecting Pneumonia Using Convolutions and Dynamic Capsule Routing for Chest X-ray Images"],"prefix":"10.3390","volume":"20","author":[{"given":"Ansh","family":"Mittal","sequence":"first","affiliation":[{"name":"Department of Computer Science &amp; Engineering, Bharati Vidyapeeth\u2019s College of Engineering, New Delhi 110063, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6690-8500","authenticated-orcid":false,"given":"Deepika","family":"Kumar","sequence":"additional","affiliation":[{"name":"Department of Computer Science &amp; Engineering, Bharati Vidyapeeth\u2019s College of Engineering, New Delhi 110063, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mamta","family":"Mittal","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, G. 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Pant Government Engineering College, New Delhi 110020, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tanzila","family":"Saba","sequence":"additional","affiliation":[{"name":"Artificial Intelligence &amp; Data Analytics (AIDA) Lab, College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ibrahim","family":"Abunadi","sequence":"additional","affiliation":[{"name":"Artificial Intelligence &amp; Data Analytics (AIDA) Lab, College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3817-2655","authenticated-orcid":false,"given":"Amjad","family":"Rehman","sequence":"additional","affiliation":[{"name":"Artificial Intelligence &amp; Data Analytics (AIDA) Lab, College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5161-9311","authenticated-orcid":false,"given":"Sudipta","family":"Roy","sequence":"additional","affiliation":[{"name":"PRT2L, Washington University in St. Louis, Saint Louis, MO 63110, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,2,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1264","DOI":"10.1016\/S0140-6736(10)61459-6","article-title":"Viral pneumonia","volume":"377","author":"Ruuskanen","year":"2011","journal-title":"Lancet"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"McLuckie, A. (2009). Respiratory disease and its management, Springer Science & Business Media.","DOI":"10.1007\/978-1-84882-095-1"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1118\/1.596412","article-title":"Image feature analysis and computer-aided diagnosis in digital radiography: Classification of normal and abnormal lungs with interstitial disease in chest images","volume":"16","author":"Katsuragawa","year":"1989","journal-title":"Med. Phys."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1457","DOI":"10.1148\/radiographics.15.6.8577968","article-title":"Fractal analysis of interstitial lung abnormalities in chest radiography","volume":"15","author":"Kido","year":"1995","journal-title":"Radiographics"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"915","DOI":"10.1118\/1.598012","article-title":"Computerized analysis of interstitial disease in chest radiographs: Improvement of geometric-pattern feature analysis","volume":"24","author":"Ishida","year":"1997","journal-title":"Med. Phys."},{"key":"ref_6","first-page":"848","article-title":"Detection of interstitial lung disease in PA chest radiographs","volume":"5368","author":"Loog","year":"2004","journal-title":"Med. Imaging Phys. Med. Imaging"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"432","DOI":"10.1053\/j.sult.2004.02.004","article-title":"Computer-aided diagnosis in chest radiology","volume":"25","author":"Abe","year":"2011","journal-title":"Semin. Ultrasound CT MRI"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-net: Convolutional networks for biomedical image segmentation. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","article-title":"Segnet: A deep convolutional encoder-decoder architecture for image segmentation","volume":"39","author":"Badrinarayanan","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Mortazi, A., Karim, R., Rhode, K., Burt, J., and Bagci, U. (2017, January 10\u201314). CardiacNET: Segmentation of left atrium and proximal pulmonary veins from MRI using multi-view CNN. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Quebec City, QC, Canada.","DOI":"10.1007\/978-3-319-66185-8_43"},{"key":"ref_11","unstructured":"Sabour, S., Frosst, N., and Hinton, G.E. (2017, January 4\u20139). Dynamic routing between capsules. Proceedings of the Advances in Neural Information Processing Systems, Long Beach, CA, USA."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"346","DOI":"10.1016\/j.asoc.2019.02.036","article-title":"Deep learning based enhanced tumor segmentation approach for MR brain images","volume":"78","author":"Mittal","year":"2019","journal-title":"Appl. Soft Comput."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Mittal, A., and Kumar, D. (2019). AiCNNs (Artificially-integrated Convolutional Neural Networks) for Brain Tumor Prediction. EAI Endorsed Trans. Pervasive Health Technol., 5.","DOI":"10.4108\/eai.12-2-2019.161976"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Mittal, M., Arora, M., Pandey, T., and Goyal, L.M. (2020). Image Segmentation Using Deep Learning Techniques in Medical Images. Advancement of Machine Intelligence in Interactive Medical Image Analysis, Springer.","DOI":"10.1007\/978-981-15-1100-4_3"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Afshar, P., Plataniotis, K.N., and Mohammadi, A. (2019, January 12\u201317). Capsule Networks for Brain Tumor Classification Based on Mri Images and Coarse Tumor Boundaries. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, UK.","DOI":"10.1109\/ICASSP.2019.8683759"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Mobiny, A., and Van Nguyen, H. (2018, January 16\u201320). Fast capsnet for lung cancer screening. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Granada, Spain.","DOI":"10.1007\/978-3-030-00934-2_82"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1166\/jmihi.2019.2555","article-title":"Blood Cell Image Classification Based on Image Segmentation Preprocessing and CapsNet Network Model","volume":"9","author":"Zhang","year":"2019","journal-title":"J. Med. Imaging Health Inform."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"555","DOI":"10.1016\/j.ijmedinf.2007.10.010","article-title":"Computer-aided diagnosis in chest radiography for detection of childhood pneumonia","volume":"77","author":"Oliveira","year":"2008","journal-title":"Int. J. Med. Inform."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2579","DOI":"10.1016\/j.procs.2013.05.444","article-title":"Comparative performance analysis of machine learning classifiers in detection of childhood pneumonia using chest radiographs","volume":"18","author":"Sousa","year":"2013","journal-title":"Procedia Comput. Sci."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1186\/s12938-018-0544-y","article-title":"Computer-aided detection in chest radiography based on artificial intelligence: A survey","volume":"17","author":"Qin","year":"2018","journal-title":"Biomed. Eng. Online"},{"key":"ref_21","first-page":"423","article-title":"Detection of pneumonia in chest X-ray images","volume":"19","author":"Parveen","year":"2011","journal-title":"J. -Ray Sci. Technol."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., and Fei, L. (2009, January 20\u201325). Imagenet: A large-scale hierarchical image database. Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref_23","unstructured":"Rajpurkar, P., Irvin, J., Zhu, K., Yang, B., Mehta, H., Duan, T., Ding, D., Bagul, A., Langlotz, C., and Shpanskaya, K. (2017). Chexnet: Radiologist-level pneumonia detection on chest x-rays with deep learning. arXiv."},{"key":"ref_24","unstructured":"Saul, C.J., Urey, D.Y., and Taktakoglu, C.D. (2019). Early Diagnosis of Pneumonia with Deep Learning. arXiv."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Ayan, E., and \u00dcnver, H.M. (2019, January 24\u201326). Diagnosis of Pneumonia from Chest X-Ray Images Using Deep Learning. Proceedings of the 2019 Scientific Meeting on Electrical-Electronics & Biomedical Engineering and Computer Science (EBBT), Istanbul, Turkey.","DOI":"10.1109\/EBBT.2019.8741582"},{"key":"ref_26","unstructured":"Islam, M.T., Aowal, M.A., Minhaz, A.T., and Ashraf, K. (2007). Abnormality detection and localization in chest X-rays using deep convolutional neural networks. arXiv."},{"key":"ref_27","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Chollet, F. (2017, January 21\u201326). Xception: Deep learning with depthwise separable convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.195"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"4168538","DOI":"10.1155\/2018\/4168538","article-title":"Deep convolutional neural networks for chest diseases detection","volume":"2018","author":"Abiyev","year":"2018","journal-title":"J. Healthc. Eng."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"4180949","DOI":"10.1155\/2019\/4180949","article-title":"An Efficient Deep Learning Approach to Pneumonia Classification in Healthcare","volume":"2019","author":"Stephen","year":"2019","journal-title":"J. Healthc. Eng."},{"key":"ref_31","unstructured":"Kermany, D.K., and Goldbaum, M. (2018). Labeled optical coherence tomography (OCT) and Chest X-Ray images for classification. Mendeley Data, 2."},{"key":"ref_32","unstructured":"Frosst, N., Sabour, S., and Hinton, G. (2018). DARCCC: Detecting adversaries by reconstruction from class conditional capsules. arXiv."},{"key":"ref_33","unstructured":"Sabour, S., Frosst, N., and Hinton, G. (May, January 30). Matrix capsules with EM routing. Proceedings of the 6th International Conference on Learning Representations, ICLR, Vancouver, BC, Canada."},{"key":"ref_34","unstructured":"Qin, Y., Frosst, N., Sabour, S., Raffel, C., Cottrell, G., and Hinton, G. (2019). Detecting and Diagnosing Adversarial Images with Class-Conditional Capsule Reconstructions. arXiv."},{"key":"ref_35","unstructured":"Kosiorek, A.R., Sabour, S., Teh, Y.W., and Hinton, G. (2019, January 8\u201314). Unsupervised Object Discovery via Capsule Decoders. Proceedings of the Advances in Neural Information Processing Systems 32 (NIPS 2019), Vancouver, BC, Canada."},{"key":"ref_36","unstructured":"Pedamonti, D. (2018). Comparison of nonlinear activation functions for deep neural networks on MNIST classification task. arXiv."},{"key":"ref_37","unstructured":"Oliphant, T.E. (2006). A Guide to NumPy, Trelgol Publishing."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1109\/MCSE.2007.55","article-title":"Matplotlib: A 2D graphics environment","volume":"9","author":"Hunter","year":"2007","journal-title":"Comput. Sci. Eng."},{"key":"ref_39","unstructured":"Gulli, A., and Pal, S. (2017). Deep Learning with Keras, Packt Publishing Ltd."},{"key":"ref_40","unstructured":"Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., and Devin, M. (2016). Tensorflow: Large-scale machine learning on heterogeneous distributed systems. arXiv."},{"key":"ref_41","unstructured":"Kurbiel, T., and Khaleghian, S. (2017). Training of Deep Neural Networks based on Distance Measures using RMSProp. arXiv."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"828","DOI":"10.1109\/TEVC.2019.2890858","article-title":"One pixel attack for fooling deep neural networks","volume":"23","author":"Su","year":"2019","journal-title":"IEEE Trans. Evol. Comput."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/j.neuroimage.2008.04.239","article-title":"Population dynamics: Variance and the sigmoid activation function","volume":"42","author":"Marreiros","year":"2008","journal-title":"Neuroimage"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Zhang, W., Tang, P., and Zhao, L. (2019). Remote Sensing Image Scene Classification Using CNN-CapsNet. Remote. Sens., 11.","DOI":"10.3390\/rs11050494"},{"key":"ref_45","unstructured":"Hoogi, A., Wilcox, B., Gupta, Y., and Rubin, D.L. (2019). Self-Attention Capsule Networks for Image Classification. arXiv."},{"key":"ref_46","unstructured":"Shang, W., Sohn, K., Almeida, D., and Lee, H. (2016, January 20\u201322). Understanding and improving convolutional neural networks via concatenated rectified linear units. Proceedings of the Intzrnational Conference on Machine Learning, New York, NY, USA."},{"key":"ref_47","unstructured":"Rosario, V.M.d., Borin, E., and Breternitz, M. (2019). The Multi-Lane Capsule Network (MLCN). arXiv."},{"key":"ref_48","unstructured":"Sun, Y., Xue, B., Zhang, M., and Yen, G.G. (2018). Automatically designing CNN architectures using genetic algorithm for image classification. arXiv."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Sun, Y., Xue, B., Zhang, M., and Yen, G.G. (2019). Evolving deep convolutional neural networks for image classification. IEEE Trans. Evol. Comput.","DOI":"10.1109\/TEVC.2019.2916183"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Castillo, P.A., Arenas, M.G., Castillo-Valdivieso, J.J., Merelo, J.J., Prieto, A., and Romero, G. (2003). Artificial neural networks design using evolutionary algorithms. Advances in Soft Computing, Springer.","DOI":"10.1007\/978-1-4471-3744-3_5"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1007\/s10462-011-9270-6","article-title":"Evolutionary artificial neural networks: A review","volume":"39","author":"Ding","year":"2013","journal-title":"Artif. Intell. Rev."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1162\/106365601750190398","article-title":"Completely derandomized self-adaptation in evolution strategies","volume":"9","author":"Hansen","year":"2001","journal-title":"Evol. Comput."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Hansen, N. (2006). The CMA evolution strategy: A comparing review. Towards a New Evolutionary Computation, Springer.","DOI":"10.1007\/3-540-32494-1_4"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"33240","DOI":"10.1109\/ACCESS.2019.2902579","article-title":"An Efficient Edge Detection Approach to Provide Better Edge Connectivity for Image Analysis","volume":"7","author":"Mittal","year":"2019","journal-title":"IEEE Access"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/4\/1068\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T08:58:11Z","timestamp":1760173091000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/4\/1068"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,2,15]]},"references-count":54,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2020,2]]}},"alternative-id":["s20041068"],"URL":"https:\/\/doi.org\/10.3390\/s20041068","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,2,15]]}}}