Multi-Channel Based Image Processing Scheme for Pneumonia Identification

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.

How much BiGAN and CycleGAN-learned hidden features are effective for COVID-19 detection from CT images? A comparative study

“…A large number of research papers adopt supervised learning methods for the reliable detection of COVID-19 diseases [6][7][8][9][10][11][12][13][14][15][16] [17][18][19][20]. However, due to the lack of publicly available CTs on COVID-19 patients, researchers have been triggered to consider this deficiency, especially at the beginning of the spread of COVID-19.…”

Identification of Diabetic Retinopathy Using Weighted Fusion Deep Learning Based on Dual-Channel Fundus Scans

“…Undoubtedly, DL can uncover hidden elements in images that medical specialists would never see. Due to its capability in feature extraction and training in discriminating between multi-classes, the convolutional neural network (CNN) is the most commonly used DL approach in the medical system [ 12 ]. On several medical datasets, the transfer learning (TL) approach has also made it easier to retrain deep neural networks quickly and reliably [ 13 , 14 ].…”

COVID-19 Identification from Low-Quality Computed Tomography Using a Modified Enhanced Super-Resolution Generative Adversarial Network Plus and Siamese Capsule Network

“…For pulmonary field segmentation, this device uses a U-Net pre-processor model; after that, a 3D ResNet50 architecture with ImageNet weights was transferred. According to the authors in [ 21 ], they proposed using three different channels of CXR images with their individual deep neural networks. Thus, the final feature weights of the three channels are concatenated and softmax classification is utilized to determine the final classification of COVID-19 and other pneumonia.…”