Ansh Mittal1, Deepika Kumar1, Mamta Mittal2, Tanzila Saba3, Ibrahim Abunadi3, Amjad Rehman3, Sudipta Roy4. 1. Department of Computer Science & Engineering, Bharati Vidyapeeth's College of Engineering, New Delhi 110063, India. 2. Department of Computer Science and Engineering, G. B. Pant Government Engineering College, New Delhi 110020, India. 3. Artificial Intelligence & Data Analytics (AIDA) Lab, College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586 Saudi Arabia. 4. PRT2L, Washington University in St. Louis, Saint Louis, MO 63110, USA.
Abstract
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.
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 n class="Disease">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.
Entities:
Keywords:
chest X-Ray (CXR); deep learning; pneumonia; simple CapsNet
Authors: Mohd Asyraf Zulkifley; Nur Ayuni Mohamed; Siti Raihanah Abdani; Nor Azwan Mohamed Kamari; Asraf Mohamed Moubark; Ahmad Asrul Ibrahim Journal: Diagnostics (Basel) Date: 2021-04-24