A quantum-clustering optimization method for COVID-19 CT scan image segmentation. (15th December 2021)

• Record Type: Journal Article
• Authors: Singh, Pritpal; Bose, Surya Sekhar
• Is Part Of: Expert systems with applications. Volume 185(2021)
• Publication Date: 2021-12-15
• Subjects: Coronavirus Disease 2019 (COVID-19) -- K-means clustering (KMC) algorithm -- Fast forward quantum optimization algorithm (FFQOA) -- Computed tomography (CT) images -- Image segmentation; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2021.115637
• Languages: English
• ISSNs: 0957-4174

Abstract

Abstract: The World Health Organization (WHO) has declared Coronavirus Disease 2019 (COVID-19) as one of the highly contagious diseases and considered this epidemic as a global health emergency. Therefore, medical professionals urgently need an early diagnosis method for this new type of disease as soon as possible. In this research work, a new early screening method for the investigation of COVID-19 pneumonia using chest CT scan images has been introduced. For this purpose, a new image segmentation method based on K-means clustering algorithm (KMC) and novel fast forward quantum optimization algorithm (FFQOA) is proposed. The proposed method, called FFQOAK (FFQOA+KMC), initiates by clustering gray level values with the KMC algorithm and generating an optimal segmented image with the FFQOA. The main objective of the proposed FFQOAK is to segment the chest CT scan images so that infected regions can be accurately detected. The proposed method is verified and validated with different chest CT scan images of COVID-19 patients. The segmented images obtained using FFQOAK method are compared with various benchmark image segmentation methods. The proposed method achieves mean squared error, peak signal-to-noise ratio, Jaccard similarity coefficient and correlation coefficient of 712.30, 19.61, 0.90 and 0.91 in case of four experimental sets, namely Experimental_Set_1, Experimental_Set_2, Experimental_Set_3 and Experimental_Set_4, respectively. These four performance evaluation metrics show the effectiveness of FFQOAK method over these existing methods. Highlights: This study introduces the novel fast forward quantum optimization algorithm (FFQOA). The FFQOA is hybridized with the K-means clustering (KMC) algorithm. The FFQOAK (FFQOA+KMC) is applied in the segmentation of chest CT images of COVID-19 patients. Performance evaluation metrics indicate the effectiveness of the proposed algorithm. The proposed algorithm is able to recognize the infected regions effectively.