A new approach for classifying coronavirus COVID-19 based on its manifestation on chest X-rays using texture features and neural networks

Abstract

Since the recent challenge that humanity is facing against COVID-19, several initiatives have been put forward with the goal of creating measures to help control the spread of the pandemic. In this paper we present a series of experiments using supervised learning models in order to perform an accurate classification on datasets consisting of medical images from COVID-19 patients and medical images of several other related diseases affecting the lungs. This work represents an initial experimentation using image texture feature descriptors, feed-forward and convolutional neural networks on newly created databases with COVID-19 images. The goal was setting a baseline for the future development of a system capable of automatically detecting the COVID-19 disease based on its manifestation on chest X-rays and computerized tomography images of the lungs.

Keywords: COVID-19; GLCM; Image Classification; Neural Networks; Pneumonia; X-ray.

Fig. 1

COVID-19 labeled images in Cohen’s Database.

Fig. 2

Example of GLCM Creation.

Fig. 3

Feature vector creation from Local Binary Patterns (LBP).

Fig. 4

Architectures used for experimentation.

Fig. 5

Examples of the 6 classes.

Fig. 6

Examples of the 10 classes.

Fig. 7

Examples of the 2 classes from the custom dataset.

Fig. 8

Examples of the 3 classes from the custom dataset.