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

• Published in: Applied intelligence (Dordrecht, Netherlands) Vol. 51; no. 1; pp. 571 - 585
• Main Authors: Ahuja, Sakshi, Panigrahi, Bijaya Ketan, Dey, Nilanjan, Rajinikanth, Venkatesan, Gandhi, Tapan Kumar
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.01.2021; Springer Nature B.V
• Subjects: Artificial Intelligence; Artificial Intelligence Applications for COVID-19; Computed tomography; Computer Science; Control; Coronaviruses; COVID-19; Detection; Diagnosis; Disease; Lungs; Machine learning; Machines; Manufacturing; Mechanical Engineering; Medical imaging; Model accuracy; Prediction; Processes; Respiratory system; Severe acute respiratory syndrome coronavirus 2; Viral diseases; COVID-19; Wavelets; ResNet18; Transfer learning; CT scan
• ISSN: 0924-669X; 1573-7497
• DOI: 10.1007/s10489-020-01826-w

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.