Ahmed Ibrahim Abdulrab, Senan Ebrahim Mohammed, Shatnawi Hamzeh Salameh Ahmad, Alkhraisha Ziad Mohammad, Al-Azzam Mamoun Mohammad Ali
Computer Department, Applied College, Najran University, Najran 66462, Saudi Arabia.
Department of Artificial Intelligence, Faculty of Computer Science and Information Technology, Alrazi University, Sana'a, Yemen.
Diagnostics (Basel). 2023 Feb 20;13(4):814. doi: 10.3390/diagnostics13040814.
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%.
一种名为肺结核（TB）的传染病表现出类似肺炎的症状和特征。X射线成像检查是鉴别诊断肺炎和肺结核最重要的手段之一。然而，由于肺炎和肺结核症状相似，放射科医生和临床医生很难进行早期鉴别。因此，患者无法得到恰当的治疗，疾病也无法得到有效控制。本研究旨在通过多种技术提取混合特征，以期在肺炎和肺结核的鉴别诊断中取得理想的结果。本研究提出了几种早期识别和区分肺结核与肺炎的方法。第一种用于区分肺炎和肺结核的系统采用混合技术，即VGG16+支持向量机（SVM）和ResNet18+SVM。第二种用于区分肺炎和肺结核的系统是基于VGG16和ResNet18特征融合的人工神经网络（ANN），并在使用主成分分析（PCA）方法进行高维降维前后进行特征融合。第三种用于区分肺炎和肺结核的系统是基于VGG16和ResNet18特征分别与通过局部二值模式（LBP）、离散小波变换（DWT）和灰度共生矩阵（GLCM）算法提取的手工特征进行融合的人工神经网络。所有提出的系统在肺炎和肺结核的早期鉴别中均取得了优异的结果。基于VGG16特征与LBP、DWT和GLCM（LDG）的人工神经网络的准确率达到99.6%，灵敏度达到99.17%，特异性达到99.42%，精确率达到99.63%，曲线下面积（AUC）达到99.58%。
