Classification of Covid-19 X-Ray Images Using Fuzzy Gabor Filter and DCNN

Abstract

The rapid growth in Covid-19 cases increases the burden on health care services all over the world. Hence, a quicker and accurate diagnosis of this disease is essential in this situation. To get quick and accurate results, X-ray images are commonly used. Deep Learning (DL) techniques have reached a high position since they provide accurate results for medical imaging applications and regression problems. However the pre-processing methods are not successful in eliminating the impulse noises and the feature extraction technique involving filtering methods did not yield good filter response. In this paper, Covid-19 X-ray images were classified using the Fuzzy Gabor filter and Deep Convolutional Neural Network (DCNN). Initially the Chest X-ray images are pre-processed using Median Filters. After pre-processing, a Fuzzy Gabor filter is applied for feature extraction. Local vector features were first extracted from the given image using the Gabor filter, taking these vectors as observations. The orientation and wavelengths of the Gabor filter were fuzzified to improve the filter response. The extracted features are then trained and classified using the DCNN algorithm. It classifies the chest X-ray images into three categories that includes Covid-19, Pneumonia and normal. Experimental results have shown that the proposed Fuzzy Gabor-CNN algorithm attains highest accuracy, Precision, Recall and F1-score when compared to existing feature extraction and classification techniques.