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

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.

A Novel Method to Identify Pneumonia through Analyzing Chest Radiographs Employing a Multichannel Convolutional Neural Network

“…The study described in [41] used transfer learning on the dataset referenced in [42] and, after performing a four-class classification, obtained an accuracy of 92.80%. The outcomes of the studies described in [43][44][45] are directly comparable to this work, since they all perform binary classifications and employ the same dataset. As the table shows, our method outperforms them by 4.19%, 1.56%, and 1.53%, respectively, in terms of classification accuracy.…”

DeepFake Video Detection: A Time-Distributed Approach

“…Image segmentation has been found useful for various purposes [22][23][24]. In addition, specific techniques utilized in the medical domain and watermarking [25][26][27] could be used as a reference for forgery detection.…”

Pneumonia Detection using Depth-Wise Convolutional Neural Network (DW-CNN)

“…In another study, a CNN model was proposed which could accurately detect pneumonia from chest radiograph samples and it also deployed data augmentation techniques in order to improve the accuracy of the model [12][13]. As per study, a model was trained using a dataset which consisted of 112,120 images collected from 30,805 unique patients [14].…”

“…They first extracted the lung region using FCN model and DCNN method by using the dataset from public JSRT Database [22] and MC dataset [23] The authors have proposed a multi-layered capsule called capsNet to reorganize pneumonia from chest radiograph images. An ICC & ECC (Integration & Ensemble of convolutions) has been proposed to detect pneumonia [24]. Rajpurkar et al [25] proposed a new 121 sheets CNN which classified pneumonia among 14 other diseases based on Chest X rays, called cheXNet.…”