Detection of coronavirus Disease (COVID-19) based on Deep Features and Support Vector Machine

Abstract

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 feature plus support vector machine (SVM) based methodology is suggested for detection of coronavirus infected patient using X-ray images. For classification, SVM is used instead of deep learning based classifier, as the later one need a large dataset for training and validation. The deep features from the fully connected layer of CNN model are extracted and fed to SVM for classification purpose. The SVM classifies the corona affected X-ray images from others. The methodology consists of three categories of Xray images, i.e., COVID-19, pneumonia and normal. The method is beneficial for the medical practitioner to classify among the COVID-19 patient, pneumonia patient and healthy people. SVM is evaluated for detection of COVID-19 using the deep features of different 13 number of CNN models. The SVM produced the best results using the deep feature of ResNet50. The classification model, i.e. ResNet50 plus SVM achieved accuracy, sensitivity, FPR and F1 score of 95.33%,95.33%,2.33% and 95.34% respectively for detection of COVID-19 (ignoring SARS, MERS and ARDS). Again, the highest accuracy achieved by ResNet50 plus SVM is 98.66%. The result is based on the Xray images available in the repository of GitHub and Kaggle. As the data set is in hundreds, the classification based on SVM is more robust compared to the transfer learning approach. Also, a comparison analysis of other traditional classification method is carried out. The traditional methods are local binary patterns (LBP) plus SVM, histogram of oriented gradients (HOG) plus SVM and Gray Level Co-occurrence Matrix (GLCM) plus SVM. In traditional image classification method, LBP plus SVM achieved 93.4% of accuracy.

COVID-19 open source data sets: a comprehensive survey

“…Similar to Sethy et al [51], Afshar et al [32] also negated the applicability of DNNs on small COVID-19 data sets. The authors proposed a capsule network model (COVID-CAPS) for the diagnosis of COVID-19 based on X-ray images.…”

“…A combination of Decision tree and Adaboost were employed for classification with 83.9% accuracy. Figure 2 depicts a generic work-flow of ML based COVID-19 diagnosis [51,52]. The data set containing medical images is pre-processed with segmentation and augmentation techniques if necessary.…”

“…Authors in [51] propose Support Vector Machine (SVM) based classification of X-ray images instead of predominately employed deep learning models. The authors argue that deep learning models require large data sets for training that are not available currently for COVID-19 cases.…”

InstaCovNet-19: A deep learning classification model for the detection of COVID-19 patients using Chest X-ray

“…This deep domain adaption algorithm achieved an AUC of 0.985 and an F1-Score of 92.98%. In [28] , the authors utilized a pre-trained ResNet50 for feature extraction and SVM for classification and achieved an accuracy of 95.38% on binary classification. In [29] , the authors used DarkNet and different filtering on each layer and achieved an accuracy of 98.08% on binary classification and 87.02% in the classification of x-ray images as Pneumonia, Covid-19 and Normal.…”

CCBlock based on deep learning for diagnosis COVID-19 in chest x-ray image

“…ResNet50, InceptionV3, and Inception ResNetV2) to predict a small dataset. The study by Prabira Kumar Sethy et al [23] differs from the abovementioned studies in terms of the research strategy because they first extracted the deep features through a deep convolutional network (ResNet50) and then classified the COVID-19 cases based on the remaining chest x-ray images by using a support vector machine (SVM). Due to the data insufficiency, Pedro Bassi et al [24] adopted the transfer learning strategy and developed a deep neural network (CheXNet) which was pre-trained with images of 14 chest diseases.…”