Application of fuzzy logic on CT-scan images of COVID-19 patients. (8th September 2021)

• Record Type: Journal Article
• Authors: Noor, Fariha; Tanjim, Md. Rashad; Rahim, Muhammad Jawadur; Suvon, Md. Naimul Islam; Porna, Faria Karim; Ahmed, Shabbir; Kaioum, Md. Abdullah Al; Rahman, Rashedur M.
• Is Part Of: International journal of intelligent information and database systems. Volume 14:Number 4(2021)
• Page Start: 333
• Page End: 348
• Publication Date: 2021-09-08
• Subjects: medical image processing -- fuzzy c-means clustering -- image segmentation -- convolutional neural network; Database management -- Computer programs -- Periodicals; Information retrieval -- Computer programs -- Periodicals; Information storage and retrieval systems -- Computer programs -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Intelligent agents (Computer software) -- Periodicals; 006.33
• Languages: English
• ISSNs: 1751-5858

Abstract

Image processing is crucial in any image analysis to determine the problem. If it is a medical area, a suitable image processing method becomes even more imperative to get as accurate results as possible. Due to the widespread outbreak of coronavirus disease 2019 (COVID-19), an infectious respiratory disease, it has become quite urgent that a reliable method for identification of the disease is sought. In this paper, we have segmented images with two different techniques, fuzzy c -means, and k -means clustering. Our images include CT-scan data and X-rays of both two categories. The first being the COVID-19 infected patients; the other being a collection of normal persons, and viral pneumonia infected persons. Among the two clustering techniques, the k -means performed better. Later, we trained our CNN model with the segmented images and raw images. Interestingly, the segmented images of CT-scan, as well as X-rays, are performing well in CNN classification rather than raw images. After applying fuzzy edge detection, the segmentation was improved. The f1-score for our model is 91% and the support is 89%.