Next Article in Journal
NaviSoC: High-Accuracy Low-Power GNSS SoC with an Integrated Application Processor
Previous Article in Journal
Microwave Differential Frequency Splitting Sensor Using Magnetic-LC Resonators
Previous Article in Special Issue
Aging with Autism Departs Greatly from Typical Aging
Open AccessArticle

Detecting Pneumonia using Convolutions and Dynamic Capsule Routing for Chest X-ray Images

Department of Computer Science & Engineering, Bharati Vidyapeeth’s College of Engineering, New Delhi 110063, India
Department of Computer Science and Engineering, G. B. Pant Government Engineering College, New Delhi 110020, India
Artificial Intelligence & Data Analytics (AIDA) Lab, College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586 Saudi Arabia
PRT2L, Washington University in St. Louis, Saint Louis, MO 63110, USA
Authors to whom correspondence should be addressed.
Sensors 2020, 20(4), 1068;
Received: 12 January 2020 / Revised: 7 February 2020 / Accepted: 7 February 2020 / Published: 15 February 2020
(This article belongs to the Special Issue Machine Learning for Biomedical Imaging and Sensing)
An entity’s 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—Integration of convolutions with capsules (ICC) and Ensemble of convolutions with capsules (ECC)—detect 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.
Keywords: pneumonia; chest X-Ray (CXR); simple CapsNet; deep learning pneumonia; chest X-Ray (CXR); simple CapsNet; deep learning
MDPI and ACS Style

Mittal, A.; Kumar, D.; Mittal, M.; Saba, T.; Abunadi, I.; Rehman, A.; Roy, S. Detecting Pneumonia using Convolutions and Dynamic Capsule Routing for Chest X-ray Images. Sensors 2020, 20, 1068.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Back to TopTop