Artificial Intelligence Framework for Efficient Detection and Classification of Pneumonia Using Chest Radiography Images

Ali Mohammad Alqudah,Ihssan S Masad,Shoroq Qazan

doi:10.1007/s40846-021-00631-1

Ali Mohammad Alqudah, Ihssan S Masad + Show 1 more

Open Access

https://doi.org/10.1007/s40846-021-00631-1

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Abstract

BackgroundChest diseases are serious health problems that threaten the lives of people. The early and accurate diagnosis of such diseases is very crucial in the success of their treatment and cure. Pneumonia is one of the most widely occurred chest diseases responsible for a high percentage of deaths especially among children. So, detection and classification of pneumonia using the non-invasive chest x-ray imaging would have a great advantage of reducing the mortality rates.ResultsThe results showed that the best input image size in this framework was 64 × 64 based on comparison between different sizes. Using CNN as a deep features extractor and utilizing the tenfold methodology the propose artificial intelligence framework achieved an accuracy of 94% for SVM and 93.9% for KNN, a sensitivity of 93.33% for SVM and 93.19% for KNN and a specificity of 96.68% for SVM and 96.60% for KNN.ConclusionsIn this study, an artificial intelligence framework has been proposed for the detection and classification of pneumonia based on chest x-ray imaging with different sizes of input images. The proposed methodology used CNN for features extraction that were fed to two different types of classifiers, namely, SVM and KNN; in addition to the SoftMax classifier which is the default CNN classifier. The proposed CNN has been trained, validated, and tested using a large dataset of chest x-ray images contains in total 5852 images.

Highlights

Chest diseases are serious health problems that threaten the lives of people
The results showed that the best input image size in this framework was 64 64 based on comparison between different sizes
Using Convolutional Neural Network (CNN) as a deep features extractor and utilizing the 10-fold methodology the propose artificial intelligence framework achieved an accuracy of 94% for Support Vector Machine (SVM) and 93.9% for K-Nearest Neighbor (KNN), a sensitivity of 93.33% for SVM and 93.19% for KNN and a specificity of 96.68% for SVM and 96.60% for KNN

Summary

Introduction

Chest diseases are serious health problems that threaten the lives of people. The early and accurate diagnosis of such diseases is very crucial in the success of their treatment and cure. Detection and classification of pneumonia using the non-invasive chest x-ray imaging would have a great advantage of reducing the mortality rates. Many terms such as peripleumoniacon, pleurisy, and peripneumony were used by ancient Romans and Greeks to describe an illness which includes many conditions that are currently known as pneumonia; and later in the 19th century, a scientist called Laennec distinguished ‘pleurisy’ from pneumonia, and after that another scientist named Rokitansky recognized the bronchopneumonia and lobar pneumonia as different pathological entities [1, 2].

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