Classification of COVID-19 in Chest CT Images using Convolutional
Support Vector Machines
- URL: http://arxiv.org/abs/2011.05746v1
- Date: Wed, 11 Nov 2020 13:04:38 GMT
- Title: Classification of COVID-19 in Chest CT Images using Convolutional
Support Vector Machines
- Authors: Umut \"Ozkaya, \c{S}aban \"Ozt\"urk, Serkan Budak, Farid Melgani,
Kemal Polat
- Abstract summary: This study presents a deep learning model that detects COVID-19 cases with high performance.
The proposed method is defined as Convolutional Support Vector Machine (CSVM) and can automatically classify Computed Tomography (CT) images.
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.
- Score: 15.50817570408951
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
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