Classification of COVID-19 in Chest CT Images using Convolutional Support Vector Machines

TL;DR

This paper introduces a novel Convolutional Support Vector Machine (CSVM) model that automatically classifies COVID-19 from chest CT images, outperforming pre-trained CNNs with 94.03% accuracy in experiments.

Contribution

The study presents a CSVM model trained from scratch for COVID-19 detection in CT images, demonstrating superior performance over transfer learning CNNs.

Findings

CSVM achieved 94.03% accuracy in classification.

CSVM outperformed pre-trained CNN models.

The method shows potential for aiding COVID-19 diagnosis.

Abstract

Purpose: Coronavirus 2019 (COVID-19), which emerged in Wuhan, China and affected the whole world, has cost the lives of thousands of people. Manual diagnosis is inefficient due to the rapid spread of this virus. For this reason, automatic COVID-19 detection studies are carried out with the support of artificial intelligence algorithms. Methods: In this study, a deep learning model that detects COVID-19 cases with high performance is presented. The proposed method is defined as Convolutional Support Vector Machine (CSVM) and can automatically classify Computed Tomography (CT) images. Unlike the pre-trained Convolutional Neural Networks (CNN) trained with the transfer learning method, the CSVM model is trained as a scratch. To evaluate the performance of the CSVM method, the dataset is divided into two parts as training (%75) and testing (%25). The CSVM model consists of blocks containing three different numbers of SVM kernels. Results: When the performance of pre-trained CNN networks and CSVM models is assessed, CSVM (7x7, 3x3, 1x1) model shows the highest performance with 94.03% ACC, 96.09% SEN, 92.01% SPE, 92.19% PRE, 94.10% F1-Score, 88.15% MCC and 88.07% Kappa metric values. Conclusion: The proposed method is more effective than other methods. It has proven in experiments performed to be an inspiration for combating COVID and for future studies.