International Conference on Computer and Knowledge Engineering

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Classification of COVID-19 and Nodule in CT Images using Deep Convolutional Neural Network

Authors :

Amirhossein Ghaemi¹ Seyyed Amir Mousavi mobarakeh² Habibollah Danyali³ Kamran Kazemi⁴

1- Shiraz University of Technology 2- Shiraz University of Technology 3- Shiraz University of Technology 4- Shiraz University of Technology

Keywords :

COVID-19،Lung cancer،Nodule،Classification،Convolutional Neural Network،Data augmentation،MLP

Abstract :

Distinguishing between coronavirus disease 2019 (COVID-19) infection and nodule as an early indicator of lung cancer in Computed Tomography (CT) images has been a challenge that radiologists have faced since COVID-19 was announced as a pandemic. The similarity between these two infections is the main reason that brings dilemmas for them and may lead to a misdiagnosis. As a result, manual classification is not as efficient as automated classification. This paper proposes an automated approach to classify COVID-19 infections from nodules in CT images. Convolutional Neural Networks (CNNs) have significantly improved automated image classification tasks, particularly for medical images. Accordingly, we propose a refined CNN-based architecture through modifications in the network layers to reduce complexity. Furthermore, data augmentation techniques are utilized to overcome the lack of training data. In our method, Multi Layer Perceptron (MLP) is obligated to categorize the feature vectors extracted from denoised input images by convolutional layers into two main classes of COVID-19 infections and nodules. To the best of our knowledge, other state-of-the-art methods can only classify one of the two classes listed above. Compared to the mentioned counterparts, our proposed method has a promising performance with an accuracy of 97.80%.

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