Multi-Techniques for Analyzing X-ray Images for Early Detection and Differentiation of Pneumonia and Tuberculosis Based on Hybrid Features

Abstract

An infectious disease called tuberculosis (TB) exhibits pneumonia-like symptoms and traits. One of the most important methods for identifying and diagnosing pneumonia and tuberculosis is X-ray imaging. However, early discrimination is difficult for radiologists and doctors because of the similarities between pneumonia and tuberculosis. As a result, patients do not receive the proper care, which in turn does not prevent the disease from spreading. The goal of this study is to extract hybrid features using a variety of techniques in order to achieve promising results in differentiating between pneumonia and tuberculosis. In this study, several approaches for early identification and distinguishing tuberculosis from pneumonia were suggested. The first proposed system for differentiating between pneumonia and tuberculosis uses hybrid techniques, VGG16 + support vector machine (SVM) and ResNet18 + SVM. The second proposed system for distinguishing between pneumonia and tuberculosis uses an artificial neural network (ANN) based on integrating features of VGG16 and ResNet18, before and after reducing the high dimensions using the principal component analysis (PCA) method. The third proposed system for distinguishing between pneumonia and tuberculosis uses ANN based on integrating features of VGG16 and ResNet18 separately with handcrafted features extracted by local binary pattern (LBP), discrete wavelet transform (DWT) and gray level co-occurrence matrix (GLCM) algorithms. All the proposed systems have achieved superior results in the early differentiation between pneumonia and tuberculosis. An ANN based on the features of VGG16 with LBP, DWT and GLCM (LDG) reached an accuracy of 99.6%, sensitivity of 99.17%, specificity of 99.42%, precision of 99.63%, and an AUC of 99.58%.

Keywords: ANN; DWT; GLCM; LBP; PCA; ResNet18; VGG16; tuberculosis.

Figure 1

The framework of the structure for the proposed systems for the diagnosis of X-rays of pneumonia and tuberculosis, and for distinguishing between them.

Figure 2

Samples from the pneumonia and tuberculosis data set. (a). Before improving the X-rays. (b). After improving the X-rays.

Figure 3

Structure framework of the hybrid system for early diagnosis and discrimination between pneumonia and tuberculosis.

Figure 4

The structure of the early diagnosis technique and the discrimination of pneumonia and tuberculosis by ANN, based on the fusion of CNN features.

Figure 5

Structure framework of the technique for early diagnosis and discrimination of pneumonia and tuberculosis by ANN, based on the fusion of CNN features with hand-crafted features.

Figure 6

The number of X-rays of pneumonia and tuberculosis in the data set before and after applying the data augmentation technique.

Figure 7

The performance of the hybrid methods for diagnosing the X-rays of the pneumonia and tuberculosis data set.

Figure 8

The confusion matrix displays the X-ray results for diagnosing pneumonia and tuberculosis using (a). VGG16 + SVM; (b). ResNet18 + SVM.

Figure 9

The ANN performance based on integrating the features for the X-ray diagnostics of the pneumonia and tuberculosis data set.

Figure 10

Confusion matrix displaying the ANN results for the diagnosis of X-rays of pneumonia and tuberculosis based on deep feature integration, (a). VGG16 + ResNet18 before PCA algorithm; (b). VGG16 + ResNet18 after PCA algorithm.

Figure 11

The error histogram for diagnosing the X-ray images of pneumonia and tuberculosis using the ANN with features (a). VGG16 and LDG; (b). ResNet18 and LDG.

Figure 12

The best validation performance for diagnosing X-ray images of pneumonia and tuberculosis using the ANN with features (a). VGG16 and LDG; (b). ResNet18 and LDG.

Figure 13

The GVC for diagnosing X-ray images of pneumonia and tuberculosis using the ANN with features (a). VGG16 and LDG; (b). ResNet18 and LDG.

Figure 14

Confusion matrix for diagnosing the X-ray images of pneumonia and tuberculosis using the ANN with features (a). VGG16 and LDG; (b). ResNet18 and LDG.

Figure 15

ANN achievement based on fusing the features of CNN with hand-crafted features for diagnosing the X-rays of the pneumonia and tuberculosis data set.

Figure 16

The execution and differentiation of the proposed methods for diagnosing the X-rays of the pneumonia and tuberculosis data set.