CNNFDNF: CONVOLUTIONAL NEURAL NETWORK AND CASCADE NEURO FUZZY NETWORK FOR SEVERITY LEVEL OF COVID-19 DETECTION USING CHEST X-RAY IMAGES

Abstract

Chest X-ray (CXR) images are the most important imaging modality to monitor and detect several lung diseases and Coronavirus Disease 2019 (COVID-19) disease. This is the first imaging method in the detection of COVID-19. Since the limited availability of interpreted medical images, and the accuracy are the biggest challenges in the conventional methods. Hence, a novel framework is designed for the severity level of COVID-19 detection using CXR images named Convolutional Neural Network and Cascade Neuro-Fuzzy Network (CNNFDNF). Initially, the input CXR image is allowed to the preprocessing phase that is done utilizing the Kalman filter and Region of Interest (ROI) extraction. Then, the lung lobe segmentation is executed by Psi-Net. After that, the extraction of Convolutional Neural Network (CNN) features is conducted from the interested lobe region. Moreover, the extracted image features are subjected to COVID-19 detection, where it is achieved by Deep Generative Adversarial Network (Deep GAN) trained by Henry Gas Water Wave Optimization (HGWWO). Here, HGWWO is blended by Water Wave Optimization (WWO) and Henry Gas Solubility Optimization (HGSO). The detection phase is categorized into COVID and non-COVID. If the detected image is COVID, then the CXR images are given to the second level classification. Finally, it is accomplished by employing the proposed CNNFDNF to classify them into low, moderate, or high COVID-19 infection affected at lungs. The proposed CNNFDNF is the newly devised scheme by modifying the layers of CNN and cascaded neuro-fuzzy network (DNF) structure. The measures employed for this scheme namely, accuracy, sensitivity and specificity acquired are 93.0%, 94.1%, and 93.8%. The proposed method is essential for keeping the situation under control. Because, in the severe cases may have severe pneumonia, other organ failure, and possible death.