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Published on: December 19, 2020
COVID-19 X-ray image segmentation by modified whale optimization algorithm with population reduction
Sanjoy Chakraborty1, Apu Kumar Saha2, Sukanta Nama3
1Department of Computer Science and Engineering, National Institute of Technology, Agartala, Tripura, India; Department of Computer Science and Engineering, Iswar Chandra Vidyasagar College, Belonia, Tripura, India.
Insights
This study introduces a modified whale optimization algorithm (mWOAPR) for rapid and accurate COVID-19 severity assessment from X-ray images. The enhanced algorithm improves diagnostic accuracy, aiding in timely patient treatment.
Area of Science:
- Medical Imaging
- Computational Biology
- Artificial Intelligence
Background:
- Coronavirus disease 2019 (COVID-19) significantly impacts global health and necessitates rapid diagnostic tools.
- Accurate interpretation of chest X-ray images is crucial for timely COVID-19 diagnosis and treatment planning.
- Existing diagnostic methods require enhancement for speed and precision in the context of the pandemic.
Purpose of the Study:
- To develop a computational tool for rapid and accurate COVID-19 severity assessment using chest X-ray images.
- To improve diagnostic accuracy through a modified whale optimization algorithm (WOA).
- To enhance the WOA's global search and exploitation capabilities for image segmentation.
Main Methods:
- A modified whale optimization algorithm with population reduction (mWOAPR) was developed.
- The algorithm incorporates random population initialization and adjusted parameters (A, b) for improved exploration and exploitation.
- The mWOAPR method was applied to segment benchmark images and COVID-19 chest X-ray images using multilevel thresholding and Kapur's entropy.
Main Results:
- The mWOAPR method demonstrated improved performance in segmenting benchmark images compared to basic and modified metaheuristic algorithms.
- Segmentation of COVID-19 chest X-ray images using mWOAPR showed enhanced accuracy.
- Comparative analysis confirmed the superior performance of the proposed mWOAPR over existing methods.
Conclusions:
- The developed mWOAPR algorithm offers a promising computational tool for efficient COVID-19 diagnosis from X-ray images.
- The modified optimization technique enhances the accuracy and speed of medical image analysis.
- This approach has the potential to significantly aid healthcare professionals in managing COVID-19 cases.
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.
