Image classification system for COVID-19 patient X-Ray results using median filter with deep learning method

Abstract

An image is a representation of an object that is rewritten on a medium with specific values (intensity) and has x and y coordinates. X-ray images are one type of medical image that can be used to detect and study a disease. However, X-ray images can sometimes appear blurry, making interpretation a bit challenging. Furthermore, there is variation in X-ray attenuation between normal tissue and tissue affected by a disease. By using the median filter and implementing deep learning as a method for classifying images based on feature extraction and weights in artificial neural networks. The stages carried out include preprocessing, with training and testing, feature extraction using layers of artificial neural networks that have undergone the filtering process. Then, a custom classifier layer is created to train the classification of COVID and normal classes using data from bottleneck.npy. The results of training and testing yielded an average accuracy of 95%, while the F1 Score evaluation result was 0.9."