Artificial intelligence CNN-WCA model and Weiner filtered FRFCM image segmentation technique for extraction and classification COVID-19 Virus

The COVID-19 disease started during the period December 2019 in China, and spreads rapidly throughout the world caused death of more than million peoples as per the WHO. Diagnosis of COVID-19 diseases is a very important part in its treatment. A prime reason behind an increase in the number of COVID-19 patients worldwide is the ignorance of people towards treatment in its early stages. This research work proposes a novel Weiner filter based fast and robust Fuzzy C Means (FRFCM) segmentation technique for detection of tissues from COVID-19 image and Deep CNN-WCA model for classification of diseases. As the COVID-19 images are X-Ray images, from which it is difficult to extract the COVID-19 tissues, to avoid such situation we are motivated to apply the proposed FRFCM technique. The segmented images are applied to the, proposed AI based Deep CNN-WCA (Convolutional neural network with water cycle algorithm) for classification of the type of diseased tissues for visual localization by the radiologists. Further, a future central IoT based monitoring system, we are proposing through the proposed artificial intelligence Deep CNN-WCA model to serve the patients affected by COVID-19 which will help doctors to identify and classify the covid-19 diseases with automated system. Keywords: Fuzzy C Means (FCM); Convolutional Neural Network (CNN); Artificial Intelligence (AI); Water Cycle Algorithm (WCA)