GLCMs Based multi-inputs 1D CNN Deep Learning Neural Network for COVID-19 Texture Feature Extraction and Classification

Elaf Ali Abbood, Software department, College of Information technology, University of Babylon, Hilla, IraqFollow
Tawfiq A. Al-Assadi, Software department, College of Information technology, University of Babylon, Hilla, IraqFollow

Abstract

Coronavirus disease 2019 epidemic (COVID-19) is an infectious disease that appeared because of the newest version of discovered coronavirus. The advent and rapid spread of this disease over the world necessitated a concerted effort to contain and eradicate it. Computer Tomography (CT) imaging and X-Ray images are considered as one of the important medical examinations used for disease diagnosis. To speed up and confirm the correctness of the medical diagnosis, many artificial intelligence techniques and machine learning methods are proposed. In this paper, a new and efficient proposed system is introduced to extract appropriate and meaningful features for CT scans and X-Ray COVID-19 images. The proposed method depends on extracting statistical texture features of the images using the GLCM method. The GLCMs matrices are extracted from different three quantized versions of the original image in different distances and directions. New multi-inputs 1D CNN architecture of the deep neural network is implemented to extract the effective features directly from GLCMs matrices after reducing its dimensions using the PCA technique. Three datasets are used to evaluate our method that includes SARS-CoV-2 CT-scan, COVID-CT, and DLAI3 Hackathon COVID-19 Chest X-Ray datasets. The proposed system achieved a classification improvement in terms of accuracy, F1 score, and AUC metrics compared with other methods and exceeds 98%, 89%, and 93% for three datasets, respectively.

Recommended Citation

Abbood, Elaf Ali and Al-Assadi, Tawfiq A. (2022) "GLCMs Based multi-inputs 1D CNN Deep Learning Neural Network for COVID-19 Texture Feature Extraction and Classification," Karbala International Journal of Modern Science: Vol. 8 : Iss. 1 , Article 3.
Available at: https://doi.org/10.33640/2405-609X.3201