Deep transfer learning-based automated detection of COVID-19 from lung CT scan slices
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Deep transfer learning-based automated detection of COVID-19 from lung CT scan slices Sakshi Ahuja1
· Bijaya Ketan Panigrahi1 · Nilanjan Dey2 · Venkatesan Rajinikanth3 · Tapan Kumar Gandhi1
© Springer Science+Business Media, LLC, part of Springer Nature 2020
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 · Transfer learning · Wavelets · CT scan · ResNet18
1 Introduction Lung related disease is emerged as one of the most prevalent medical conditions in humans, globally. The diseases in the lung can be categorized as, (i) airway diseases, (ii) circulation diseases, and (iii) tissue disease [1], [2], [3]. The airway diseases cause interruption of the oxygen and other gases air supply through tubes, e.g. of the disease are asthma, cystic fibrosis, Chronic obstructive pulmonary disease (COPD), Tuberculosis (TB), bronchitis, etc. The circulation diseases have an adverse effect on the flow of blood in the lungs due to the clotting inside blood vessels, e.g., pulmonary embolism and pulmonary hypertension
This article belongs to the Topical Collection: Artificial Intelligence Applications for COVID-19, Detection, Control, Prediction, and Diagnosis Sakshi Ahuja
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Extended author information available on the last page of the article.
come under this category. Lung tissue diseases are caused due to inflammation of the tissue that affects the lung expansion ability, e.g. sarcoidosis and pulmonary fibrosis. The other diseases that affect lungs are lung cancer, pneumothorax, pneumonia, and Acute Respiratory Di
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