Deep transfer learning-based automated detection of COVID-19 from lung CT scan slices

Abstract

Lung abnormality is one of the common diseases in humans of all age group and this disease may arise due to various reasons. Recently, the lung infection due to SARS-CoV-2 has affected a larger human community globally, and due to its rapidity, the World-Health-Organisation (WHO) declared it as pandemic disease. The COVID-19 disease has adverse effects on the respiratory system, and the infection severity can be detected using a chosen imaging modality. In the proposed research work; the COVID-19 is detected using transfer learning from CT scan images decomposed to three-level using stationary wavelet. A three-phase detection model is proposed to improve the detection accuracy and the procedures are as follows; Phase1- data augmentation using stationary wavelets, Phase2- COVID-19 detection using pre-trained CNN model and Phase3- abnormality localization in CT scan images. This work has considered the well known pre-trained architectures, such as ResNet18, ResNet50, ResNet101, and SqueezeNet for the experimental evaluation. In this work, 70% of images are considered to train the network and 30% images are considered to validate the network. The performance of the considered architectures is evaluated by computing the common performance measures. The result of the experimental evaluation confirms that the ResNet18 pre-trained transfer learning-based model offered better classification accuracy (training = 99.82%, validation = 97.32%, and testing = 99.4%) on the considered image dataset compared with the alternatives.

Keywords: COVID-19; CT scan; ResNet18; Transfer learning; Wavelets.

Fig. 1

Schematic diagram of the proposed methodology of COVID-19 detection

Fig. 2

Sample lung CT scan images of a COVID-19 and b Non-COVID patients

Fig. 3

Generalized block diagram of the data augmentation using wavelet decomposition up to 3 levels

Fig. 4

Augmented training data after rotation, shear, and translation operations

Fig. 5

Descriptive block diagram of ResNet18 architecture

Fig. 6

Skip connection scenario in the residual network

Fig. 7

Convergence graph of Accuracy and loss function using ResNet18 model up to epoch 50

Fig. 8

ROC characteristics curve of ResNet18

Fig. 9

GIyph plot for the performance evaluation of pre-trained transfer learning models