LBP-based information assisted intelligent system for COVID-19 identification

Abstract

A real-time COVID-19 detection system is an utmost requirement of the present situation. This article presents a chest X-ray image-based automated COVID-19 detection system which can be employed with the RT-PCR test to improve the diagnosis rate. In the proposed approach, the textural features are extracted from the chest X-ray images and local binary pattern (LBP) based images. Further, the image-based and LBP image-based features are jointly investigated. Thereafter, highly discriminatory features are provided to the classifier for developing an automated model for COVID-19 identification. The performance of the proposed approach is investigated over 2905 chest X-ray images of normal, pneumonia, and COVID-19 infected persons on various class combinations to analyze the robustness. The developed method achieves 97.97% accuracy (acc) and 99.88% sensitivity (sen) for classifying COVID-19 X-ray images against pneumonia infected and normal person's X-ray images. It attains 98.91% acc and 99.33% sen for COVID-19 X-ray against the normal X-ray classification. This method can be employed to assist the radiologists during mass screening for fast, accurate, and contact-free COVID-19 diagnosis.

Uncertainty-aware convolutional neural network for COVID-19 X-ray images classification

“…DL achieved tremendous success in the field of biomedical image analysis. Recently, many CAD systems has been developed for nodule detection [ 9 ], tumor segmentation [ 11 ], nuclei segmentation [ 12 ], image classification [ 13 ], COVID-19 disease detection [ [28] , [29] , [30] , [31] , [32] , [33] , [34] , [35] , [36] , [37] , [38] , [39] , [40] , [41] , [42] , [43] ] etc. DL methods such as deep neural networks, CNN, and dropout are used extensively.…”

“…Kaggle dataset (Dataset-C) : This dataset is obtained from the Kaggle repository [ 57 ].The Kaggle dataset contains 2905 CXR images. Out of 2905 images, 219 images belong to the COVID-19 class, 1341 images belong to the Normal class, and 1345 images belong to the Pneumonia class [ 43 , 58 ]. All the CXR images are in 1024 × 1024 size.…”

“…As can be seen in Table 10 , the proposed UA-ConvNet provides an improvement of 1.28 % in the accuracy over the existing method [ 41 ]. Similarly, for the multi-class classification task, we can compare the proposed method with the method in studies [ 35 , 43 ], as they have evaluated their method on the Dataset-A and Dataset-C, respectively. It is observed from Table 10 that the proposed method shows a significant performance improvement of 4.93 % and 1.77% in the accuracy over the existing methods [ 35 , 43 ], respectively.…”

“…Their method achieved an accuracy of 86.74% and 88.52% for VGG16 and ResNet50, respectively. Maheshwari et al [ 43 ] have extracted local binary pattern (LBP) based and image-based features from the CXR images and applied support vector machine (SVM) on the extracted features to classify them into Normal, Pneumonia, and COVID-19 classes. The authors reported a sensitivity of 97.86% for CXR image classification.…”

“…Thus, limited training data may lead to epistemic uncertainty [ 47 ]. The majority of existing approaches [ [28] , [29] , [30] , [31] , [32] , [33] , [34] , [35] , [36] , [37] , [38] , [39] , [40] , [41] , [42] , [43] ] have not provided information about the uncertainty associated with their predictions. Only a few studies [ [44] , [45] , [46] ] have provided their model uncertainty information.…”