One Shot Cluster Based Approach for the Detection of COVID-19 from Chest X-Ray Images

• DOI: 10.20944/preprints202007.0656.v1
• Publication Date: 2020-07-27T10:14:13Z
• Authors: V.N. Manjunath Ar...; Mufti Mahmud; Basant Agarwal; D.S. Guru; M. Shamim Kaiser
• Keywords: Artificial neural network; Radiology, Nuclear Medicine and Imaging; Artificial intelligence; Chest X-Ray; Cluster (spacecraft); Infectious disease (medical specialty); 02 engineering and technology; Pattern recognition (psychology); Article; Anomaly Detection in High-Dimensional Data; Artificial Intelligence; Automated Analysis of Blood Cell Images; Health Sciences; Machine learning; Pathology; 0202 electrical engineering, electronic engineering, information engineering; Disease; other; Discriminative model; Deep learning; Applications of Deep Learning in Medical Imaging; Computer science; 004; Programming language; 3. Good health; Coronavirus disease 2019 (COVID-19); Computer Science; Physical Sciences; Medicine; Computer Vision and Pattern Recognition

Corona virus disease (COVID-19) has infected over more than 10 million people around the globe and killed at least 500K worldwide by the end of June 2020. As this disease continues to evolve and scientists and researchers around the world now trying to find out the way to combat this disease in most effective way. Chest X-rays are widely available modality for immediate care in diagnosing COVID-19. Precise detection and diagnosis of COVID-19 from these chest X-rays would be practical for the current situation. This paper proposes one shot cluster based approach for the accurate detection of COVID-19 chest x-rays. The main objective of one shot learning (OSL) is to mimic the way humans learn in order to make classification or prediction on a wide range of similar but novel problems. The core constraint of this type of task is that the algorithm should decide on the class of a test instance after seeing just one test example. For this purpose we have experimented with widely known Generalized Regression and Probabilistic Neural Networks. Experiments conducted with publicly available chest x-ray images demonstrate that the method can detect COVID-19 accurately with high precision. The obtained results have outperformed many of the convolutional neural network based existing methods proposed in the literature.