Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Social-Group-Optimization Assisted Kapur’s Entropy and Morphological Segmentation for Automated Detection of COVID-19 Infection from Computed Tomography Images

Version 1 : Received: 2 May 2020 / Approved: 5 May 2020 / Online: 5 May 2020 (02:32:05 CEST)

How to cite: Dey, N.; Rajinikant, V.; Fong, S.J.; Kaiser, M.S.; Mahmud, M. Social-Group-Optimization Assisted Kapur’s Entropy and Morphological Segmentation for Automated Detection of COVID-19 Infection from Computed Tomography Images. Preprints 2020, 2020050052 (doi: 10.20944/preprints202005.0052.v1). Dey, N.; Rajinikant, V.; Fong, S.J.; Kaiser, M.S.; Mahmud, M. Social-Group-Optimization Assisted Kapur’s Entropy and Morphological Segmentation for Automated Detection of COVID-19 Infection from Computed Tomography Images. Preprints 2020, 2020050052 (doi: 10.20944/preprints202005.0052.v1).

Abstract

The Coronavirus disease (COVID-19) caused by a novel coronavirus, SARS-CoV-2, has been declared as a global pandemic. Due to its infection rate and severity, it has emerged as one of the major global threats of the current generation. To support the current combat against the disease, this research aims to propose a Machine Learning based pipeline to detect the COVID-19 infection using the lung Computed Tomography scan images (CTI). This implemented pipeline consists of a number of sub-procedures ranging from segmenting the COVID-19 infection to classifying the segmented regions. The initial part of the pipeline implements the segmentation of the COVID-19 affected CTI using Social-Group-Optimization and Kapur’s Entropy thresholding, followed by k-means clustering and morphology-based segmentation. The next part of the pipeline implements feature extraction, selection and fusion to classify the infection. PCA based serial fusion technique is used in fusing the features and the fused feature vector is then employed to train, test and validate four different classifiers namely Random Forest, k-Nearest Neighbors (KNN), Support Vector Machine with Radial Basis Function, and Decision Tree. Experimental results using benchmark datasets show a high accuracy (> 91%) for the morphology-based segmentation task and for the classification task the KNN offers the highest accuracy among the compared classifiers (> 87%). However, this should be noted that this method still awaits clinical validation, and therefore should not be used to clinically diagnose the ongoing COVID-19 infection.

Subject Areas

COVID-19 infection; CT scan image; serial feature fusion; KNN classifier; segmentation; detection accuracy

Comments (1)

Comment 1
Received: 9 May 2020
Commenter: Parikshit N Mahalle
The commenter has declared there is no conflict of interests.
Comment: Very nice work. Useful to society as well as researchers
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