Abstract
Pneumonia is a prevalent severe respiratory infection that affects the distal and alveoli airways. Across the globe, it is a serious public health issue that has caused high mortality rate of children below five years old and the aged citizens who must have had previous chronic-related ailment. Pneumonia can be caused by a wide range of microorganisms, including virus, fungus, bacteria, which varies greatly across the globe. The spread of the ailment has gained computer-aided diagnosis (CAD) attention. This paper presents a multi-channel-based image processing scheme to automatically extract features and identify pneumonia from chest X-ray images. The proposed approach intends to address the problem of low quality and identify pneumonia in CXR images. Three channels of CXR images, namely, the Local Binary Pattern (LBP), Contrast Enhanced Canny Edge Detection (CECED), and Contrast Limited Adaptive Histogram Equalization (CLAHE) CXR images are processed by deep neural networks. CXR-related features of LBP images are extracted using shallow CNN, features of the CLAHE CXR images are extracted by pre-trained inception-V3, whereas the features of CECED CXR images are extracted using pre-trained MobileNet-V3. The final feature weights of the three channels are concatenated and softmax classification is utilized to determine the final identification result. The proposed network can accurately classify pneumonia according to the experimental result. The proposed method tested on publicly available dataset reports accuracy of 98.3%, sensitivity of 98.9%, and specificity of 99.2%. Compared with the single models and the state-of-the-art models, our proposed network achieves comparable performance.
Highlights
Introduction published maps and institutional affilPneumonia is an infectious lung illness in humans that affects one or both lungs and is caused by fungus, bacteria, and viruses, among other microorganisms
Considering the evaluation performance metrics for the single model of MobileNet-V3, this model obtained accuracy of 93.7%, sensitivity of 95.4%, specificity of 95.7%, precision of 94.3%, and F1-score of 95.5%. These results further show that the Contrast Enhanced Canny Edge Detection (CECED) preprocessing technique using the MobileNet-V3 performs better than the Local Binary Pattern (LBP) shallow convolutional neural network (CNN) technique
It is worth noting that the weighted fusion of LBP, Contrast Limited Adaptive Histogram Equalization (CLAHE), and CECED chest X-rays (CXR) images result in a greater generalization ability for our proposed model
Summary
Pneumonia is an infectious lung illness in humans that affects one or both lungs and is caused by fungus, bacteria, and viruses, among other microorganisms. Pneumonia occurs as a result of pathogen-caused inflammation [1], which causes the alveoli in the lungs to fill up with pus or fluid, reducing oxygen (O2 ) and carbon-dioxide (CO2 ) exchange between the lungs and blood, making it difficult for the infected person to breathe. Other causes of pneumonia are food aspiration and chemical exposure. Patients with cancer, HIV/AIDS, hepatic disease, diabetes, cardiovascular diseases, chronic respiratory diseases, and other comorbidities, are vulnerable to pneumonia [1,2,3]. There are several methods to diagnose pneumonia, which include blood test, pulse oximetry, bronchoscopy, sputum test, pleural fluid culture, chest X-ray, magnetic resonance iations
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