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

Abstract

Pneumonia is a virulent disease that causes the death of millions of people around the world. Every year it kills more children than malaria, AIDS, and measles combined and it accounts for approximately one in five child-deaths worldwide. The invention of antibiotics and vaccines in the past century has notably increased the survival rate of Pneumonia patients. Currently, the primary challenge is to detect the disease at an early stage and determine its type to initiate the appropriate treatment. Usually, a trained physician or a radiologist undertakes the task of diagnosing Pneumonia by examining the patient’s chest X-ray. However, the number of such trained individuals is nominal when compared to the 450 million people who get affected by Pneumonia every year. Fortunately, this challenge can be met by introducing modern computers and improved Machine Learning techniques in Pneumonia diagnosis. Researchers have been trying to develop a method to automatically detect Pneumonia using machines by analyzing and the symptoms of the disease and chest radiographic images of the patients for the past two decades. However, with the development of cogent Deep Learning algorithms, the formation of such an automatic system is very much within the realms of possibility. In this paper, a novel diagnostic method has been proposed while using Image Processing and Deep Learning techniques that are based on chest X-ray images to detect Pneumonia. The method has been tested on a widely used chest radiography dataset, and the obtained results indicate that the model is very much potent to be employed in an automatic Pneumonia diagnosis scheme.

A Machine Learning Approach to Diagnosing Lung and Colon Cancer Using a Deep Learning-Based Classification Framework

“…After choosing the histopathological image dataset, features were extracted from them using two separate algorithms. The collected features were concatenated to formulate a combined feature set, and classification was performed based on it using a multi-channel CNN [ 38 ]. These steps have been described in the following subsections.…”

A Pneumonia Diagnosis Scheme Based on Hybrid Features Extracted from Chest Radiographs Using an Ensemble Learning Algorithm

“…To put the model's performance into context, we have compared it with similar methods as shown in Table 5. e first six studies of the table [1,8,[10][11][12][13][14] perform only binary classifications to determine the presence of pneumonia or its type. Hence, their reported results are not directly comparable to ours.…”

“…Mittal et al used dynamic capsule routing to achieve a maximum classification accuracy of 95.90% using the original dataset [ 11 ]. Nahid et al proposed a pneumonia detection method employing a two-channel CNN, which achieved a classification accuracy of 97.92% [ 1 ]. The authors used multiple image processing techniques to process the samples before performing the classification using the deep learning model.…”

“…As a result, breathing for the patient becomes increasingly difficult and painful. A pneumonia patient may also experience other symptoms such as fever, dry cough, vomiting, exhaustion, and chest pain [ 1 ]. The effects of pneumonia can range from mild infections to lethal organ failure, depending on the severity of the condition, the patient's age, and his/her immune system.…”

“…Machine learning-based diagnostic methods are fast, automatic, cost-efficient, and suitable for rapid utilization. Although the use of computers in disease diagnosis dates back to the 1960s, the area is currently far more promising because of the advancements in computer technology and the development of new and capable machine intelligence algorithms [ 1 ]. Especially, convolutional neural network- (CNN-) based deep learning algorithms have shown astounding success while working with high-dimensional data such as medical images and videos.…”