An Approach Towards Physics Informed Lung Ultrasound Image Scoring Neural Network for Diagnostic Assistance in COVID-19

• URL: http://arxiv.org/abs/2106.06980v1
• Date: Sun, 13 Jun 2021 13:01:53 GMT
• Title: An Approach Towards Physics Informed Lung Ultrasound Image Scoring Neural Network for Diagnostic Assistance in COVID-19
• Authors: Mahesh Raveendranatha Panicker, Yale Tung Chen, Gayathri M, Madhavanunni A N, Kiran Vishnu Narayan, C Kesavadas and A P Vinod
• Abstract summary: A novel approach is presented to extract acoustic propagation-based features to highlight the region below pleura in lung ultrasound (LUS) A neural network, referred to as LUSNet, is trained to classify the LUS images into five classes of varying severity of lung infection to track the progression of COVID-19. A detailed analysis of the proposed approach on LUS images over the infection to full recovery period of ten confirmed COVID-19 subjects shows an average five-fold cross-validation accuracy, sensitivity, and specificity of 97%, 93%, and 98% respectively over 5000 frames of COVID-19 videos.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Ultrasound is fast becoming an inevitable diagnostic tool for regular and continuous monitoring of the lung with the recent outbreak of COVID-19. In this work, a novel approach is presented to extract acoustic propagation-based features to automatically highlight the region below pleura, which is an important landmark in lung ultrasound (LUS). Subsequently, a multichannel input formed by using the acoustic physics-based feature maps is fused to train a neural network, referred to as LUSNet, to classify the LUS images into five classes of varying severity of lung infection to track the progression of COVID-19. In order to ensure that the proposed approach is agnostic to the type of acquisition, the LUSNet, which consists of a U-net architecture is trained in an unsupervised manner with the acoustic feature maps to ensure that the encoder-decoder architecture is learning features in the pleural region of interest. A novel combination of the U-net output and the U-net encoder output is employed for the classification of severity of infection in the lung. A detailed analysis of the proposed approach on LUS images over the infection to full recovery period of ten confirmed COVID-19 subjects shows an average five-fold cross-validation accuracy, sensitivity, and specificity of 97%, 93%, and 98% respectively over 5000 frames of COVID-19 videos. The analysis also shows that, when the input dataset is limited and diverse as in the case of COVID-19 pandemic, an aided effort of combining acoustic propagation-based features along with the gray scale images, as proposed in this work, improves the performance of the neural network significantly and also aids the labelling and triaging process.

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