COMPARATIVE ANALYSIS OF DEEP NEURAL NETWORK MODELS FOR PREDICTION OF COVID-19 DIAGNOSIS
Keywords:convolutional neural networks, deep learning, image classification, predicting the COVID-19 diagnosis
Background. The situation with the coronavirus COVID-19 in mid-2021 is causing concern around the world again due to the emergence of a new more dangerous strain “delta”. Attempts are being made to build mathematical models for describing the disease spread. China, the United States and other countries are developing artificial intelligence tools to help predict the COVID-19 diagnosis and are deploying them in hospitals.
Objective. The purpose of the paper is to compare how different deep convolutional neural network models deal with COVID-19 disease recognition based on chest X-rays.
Methods. The models are based on the VGG16, ResNet50 and SqueezeNet network architectures with the addition of layers to regularize these networks. The models were trained using the transfer learning technology. The quality of the models was assessed based on the confusion matrix, precision, recall, specificity and F-scores on the validation set of X-ray images.
Results. Several neural network models have been built to classify an X-ray image of the human chest into two classes: person with COVID-19 (class 1) or healthy person (class 2). The ResNet50 model achieved a fairly high precision of 96 % for class 1 and an overall specificity of 96.14 %. Recall disease detection based on this model was 88%. The VGG-16 model correctly classified 100% (Experiment 1) / 96 % (Experiment 2) of COVID-19 patients in the test sample. The patient class precision and overall specificity values based on this model, however, were lower at 89 % / 92 % and 88.46 % / 92.31 %, respectively, for the two experiments. The F-score values for the ResNet50 and VGG-16 models were quite significant and equal to 92 and 94 %.
Conclusions. Deep convolutional neural network models have shown promising results in predicting the COVID-19 diagnosis and require research. Predicting the diagnosis using the developed software based on these models takes up to one minute – this is faster than RT-PCR (reverse transcription PCR) tests, which are used to confirm the disease.
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Copyright (c) 2021 Nadezhda I. Nedashkovskaya, Oleksandr O. Sapelnikov
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