Covid-19 Chest CT Scan Image Classification Using LCKSVD and Frozen Sparse Coding

Abstract

The coronavirus disease 2019 (COVID-19) is a fast transmitting virus spreading throughout the world and causing a pandemic. Early detection of the disease is crucial in preventing the rapid propagation of the virus. Although Computed Tomography (CT) technology is not considered to be a reliable first-line diagnostic tool, it does have the potential to detect the disease. While several high performing deep learning networks have been proposed for the automated detection of the virus using CT images, deep networks lack the explainability, clearness, and simplicity of other machine learning methods. Sparse representation is an effective tool in image processing tasks with an efficient algorithm for implementation. In addition, the output sparse domain can be easily mapped to the original input signal domain, thus the features provide information about the signal in the original domain. This work utilizes two sparse coding algorithms, frozen dictionary learning, and label-consistent k-means singular value decomposition (LC-KSVD), to help classify Covid-19 CT lung images. A framework for image sparse coding, dictionary learning, and classifier learning is proposed and an accuracy of $$89\%$$ is achieved on the cleaned CC-CCII CT lung image dataset.