Differentiation of COVID-19 conditions in planar chest radiographs using optimized convolutional neural networks
- PDF / 1,940,994 Bytes
- 12 Pages / 595.276 x 790.866 pts Page_size
- 7 Downloads / 188 Views
Differentiation of COVID-19 conditions in planar chest radiographs using optimized convolutional neural networks Satyavratan Govindarajan 1
&
Ramakrishnan Swaminathan 1
Accepted: 11 September 2020 # Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract In this study, an attempt has been made to differentiate Novel Coronavirus-2019 (COVID-19) conditions from healthy subjects in Chest radiographs using a simplified end-to-end Convolutional Neural Network (CNN) model and occlusion sensitivity maps. Early detection and faster automated screening of the COVID-19 patients is essential. For this, the images are considered from publicly available datasets. Significant biomarkers representing critical image features are extracted from CNN by experimentally investigating on cross-validation methods and hyperparameter settings. The performance of the network is evaluated using standard metrics. Perturbation based occlusion sensitivity maps are employed on the features obtained from the classification model to visualise the localization of abnormal areas. Results demonstrate that the simplified CNN model with optimised parameters is able to extract significant features with a sensitivity of 97.35% and F-measure of 96.71% to detect COVID-19 images. The algorithm achieves an Area Under the Curve-Receiver Operating Characteristic score of 99.4% with Matthews correlation coefficient of 0.93. High value of Diagnostic odds ratio is also obtained. Occlusion sensitivity maps provide precise localization of abnormal regions by identifying COVID-19 conditions. As early detection through chest radiographic images are useful for automated screening of the disease, this method appears to be clinically relevant in providing a visual diagnostic solution using a simplified and efficient model. Keywords Chest radiograph . COVID-19 . Convolutional neural network . Occlusion sensitivity . Visualisation
1 Introduction Novel Coronavirus-2019 (COVID-19) is a pandemic affecting 212 countries and territories worldwide. As on May 82,020 report by the World Health Organisation, 37,59,967 confirmed cases and 2,59,474 deaths are reported [1]. The infections are seen to rise exponentially and rapidly, where an infected person transmits the disease to 406 individuals within 30 days. There is an urgent need for automated screening and early diagnosis of the disease to contain its prevalence [2]. Currently, Reverse Transcription-Polymerase Chain Reaction (RT-PCR) is the gold standard diagnostic test for confirming COVID-19 patients. However, this method has been reported to suffer from high false negative rates and is
* Satyavratan Govindarajan [email protected] 1
Non-Invasive Imaging and Diagnostics Laboratory, Biomedical Engineering Group, Department of Applied Mechanics, Indian Institute of Technology Madras, Chennai, India
time consuming [3]. Evidences of imaging manifestations show promising directions to improve sensitivity in the detection. The imaging characteristics of COVID-19 are subtly different from other types of
Data Loading...
