Semantic Pneumonia Segmentation and Classification for Covid-19 Using Deep Learning Network

Abstract

Early detection of the Covid-19 disease is essential due to its higher rate of infection affecting tens of millions of people, and its high number of deaths also by 7%. For that purpose, a proposed model of several stages was developed. The first stage is optimizing the images using dynamic adaptive histogram equalization, performing a semantic segmentation using DeepLabv3Plus, then augmenting the data by flipping it horizontally, rotating it, then flipping it vertically. The second stage builds a custom convolutional neural network model using several pre-trained ImageNet. Finally, the model compares the pre-trained data to the new output, while repeatedly trimming the best-performing models to reduce complexity and improve memory efficiency. Several experiments were done using different techniques and parameters. Accordingly, the proposed model achieved an average accuracy of 99.6% and an area under the curve of 0.996 in the Covid-19 detection. This paper will discuss how to train a customized intelligent convolutional neural network using various parameters on a set of chest X-rays with an accuracy of 99.6%.