Detection of Coronavirus Disease (COVID-19) Based on Deep Features

By Dr. Prabira Kumar Sethy, Santi Kumari Behera. 2020

662 citations recorded by the source corpus.

The detection of coronavirus (COVID-19) is now a critical task for the medical practitioner. The coronavirus spread so quickly between people and approaches 100,000 people worldwide. In this consequence, it is very much essential to identify the infected people so that prevention of spread can be taken. In this paper, the deep learning based methodology is suggested for detection of coronavirus infected patient using X-ray images. The support vector machine classifies the corona affected X-ray images from others using the deep feature. The methodology is beneficial for the medical practitioner for diagnosis of coronavirus infected patient. The suggested classification model, i.e. resnet50 plus SVM achieved accuracy, FPR, F1 score, MCC and Kappa are 95.38%,95.52%, 91.41% and 90.76% respectively for detecting COVID-19 (ignoring SARS, MERS and ARDS). The classification model ResNet50 plus SVM is superior compared to other classification models. The result is based on the data available in the repository of GitHub, Kaggle and Open-i as per their validated X-ray images.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/9fcf603143b4cc8d9b146628fdd4d36220b492ac

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In Artificial Intelligence

In Computer Vision and Pattern Recognition

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Explainable Deep Learning for Pulmonary Disease and Coronavirus COVID-19 Detection from X-rays (2020, neutral): “Sethy and Behera [44] consider deep learning models to gather features from medical images and then classiﬁed them by exploitinh the SVM classiﬁer.”
CovXNet: A multi-dilation convolutional neural network for automatic COVID-19 and other pneumonia detection from chest X-ray images with transferable multi-receptive feature optimization (2020, neutral): “[10] 25 COVID-19 + 25 Non-COVID ReNet-50/SVM 95.”
CNN-based transfer learning–BiLSTM network: A novel approach for COVID-19 infection detection (2020, background): “…Learning 98.97 Khan, Shah and Bhat [43] Transfer Learning 95.00 Gupta, Anjum, Gupta and Katarya [68] InstaCovNet-19 99.08 Sethy and Behera [69] ResNet50 + SVM 95.40 Hemdan, Shouman and Karar [70] VGG19 90.00 Rahimzadeh and Attar [71] Xception + ResNet50V2 91.40 Proposed Method Hybrid…”
Deep-chest: Multi-classification deep learning model for diagnosing COVID-19, pneumonia, and lung cancer chest diseases (2021, positive): “2 of machine learning methods that mostly focus on the automatic feature extraction and classification of images and have shown great achievement in many applications, especially in health care [4,5].”
Convolutional capsnet: A novel artificial neural network approach to detect COVID-19 disease from X-ray images using capsule networks (2020, positive): “Sethy et al. [34] extracted features from COVID-19 s X-ray images using the deep learning model and classiﬁed these features with SVM.”
Automated detection of COVID-19 cases using deep neural networks with X-ray images (2020, neutral): “Few studies conducted prior to this study have used 25 and 50 images in each class [41,42,44,45].”
Automated Deep Transfer Learning-Based Approach for Detection of COVID-19 Infection in Chest X-rays (2020, neutral): “The n-dimensional vector can be decomposed into real numbers ∈ [ 0 , 1 ] by using the Softmax function. can be computed as: Here, yj deﬁnes the input tensor’s parameter.”
Coronavirus (Covid-19) Classification Using CT Images by Machine Learning Methods (2020, methodology): “[10] ResNet50+SVM X-ray, 100 images 2 91.”
Classification of COVID-19 patients from chest CT images using multi-objective differential evolution–based convolutional neural networks (2020, background): “Sethy et al. [10] developed a deep learning model for detecting COVID-19 from X-ray images.”
Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network (2020, methodology): “CXR were used in [23] by a CNN constructed based on various ImageNet pre-trained models to extract the high level features.”
Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks (2020, background): “0 100 100 82 Fold-5 6 10 0 4 80 60 100 100 75 Mean 87 84 90 91 86 There are very few studies on literature due to the emergence of COVID-19 virus disease. Some of these are as follows: Prabira et al. [32] proposed a detection of COVID-19 using X-ray images based on deep feature and SVM. They collect”