COVID-19 X-ray image segmentation by modified whale optimization algorithm with population reduction. (December 2021)

• Record Type: Journal Article
• Authors: Chakraborty, Sanjoy; Saha, Apu Kumar; Nama, Sukanta; Debnath, Sudhan
• Is Part Of: Computers in biology and medicine. Volume 139(2021)
• Publication Date: 2021-12
• Subjects: Whale optimization algorithm -- Image segmentation -- Multilevel thresholding -- Kapur's entropy -- COVID-19 chest X-ray image; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2021.104984
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Coronavirus disease 2019 (COVID-19) has caused a massive disaster in every human life field, including health, education, economics, and tourism, over the last year and a half. Rapid interpretation of COVID-19 patients' X-ray images is critical for diagnosis and, consequently, treatment of the disease. The major goal of this research is to develop a computational tool that can quickly and accurately determine the severity of an illness using COVID-19 chest X-ray pictures and improve the degree of diagnosis using a modified whale optimization method (WOA). To improve the WOA, a random initialization of the population is integrated during the global search phase. The parameters, coefficient vector (A) and constant value (b), are changed so that the algorithm can explore in the early stages while also exploiting the search space extensively in the latter stages. The efficiency of the proposed modified whale optimization algorithm with population reduction (mWOAPR) method is assessed by using it to segment six benchmark images using multilevel thresholding approach and Kapur's entropy-based fitness function calculated from the 2D histogram of greyscale images. By gathering three distinct COVID-19 chest X-ray images, the projected algorithm (mWOAPR) is utilized to segment the COVID-19 chest X-ray images. In both benchmark pictures and COVID-19 chest X-ray images, comparisons of the evaluated findings with basic and modified forms of metaheuristic algorithms supported the suggested mWOAPR's improved performance. Highlights: Search prey phase of WOA is replaced with random initialization of solution. Value of co-efficient vector A and constant b used in exploitation phase is altered. A new parameter β is used to select between exploration and exploitation. Population reduction technique is used to lessen the computational burden. Proposed algorithm is used to detect COVID-19 by segmenting chest X-Ray images.