Deep Learning Based Automated COVID-19 Classification from Computed Tomography Images

• URL: http://arxiv.org/abs/2111.11191v1
• Date: Mon, 22 Nov 2021 13:35:10 GMT
• Title: Deep Learning Based Automated COVID-19 Classification from Computed Tomography Images
• Authors: Kenan Morani, Devrim Unay
• Abstract summary: The paper presents a Convolutional Neural Networks (CNN) model for image classification, aiming at increasing predictive performance for COVID-19 diagnosis. This work proposes a less complex solution based on simply classifying 2D CT-Scan slices of images using their pixels via a 2D CNN model. Despite the simplicity in architecture, the proposed model showed improved quantitative results exceeding state-of-the-art on the same dataset of images.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The paper presents a Convolutional Neural Networks (CNN) model for image classification, aiming at increasing predictive performance for COVID-19 diagnosis while avoiding deeper and thus more complex alternatives. The proposed model includes four similar convolutional layers followed by a flattening and two dense layers. This work proposes a less complex solution based on simply classifying 2D CT-Scan slices of images using their pixels via a 2D CNN model. Despite the simplicity in architecture, the proposed model showed improved quantitative results exceeding state-of-the-art on the same dataset of images, in terms of the macro f1 score. In this case study, extracting features from images, segmenting parts of the images, or other more complex techniques, ultimately aiming at images classification, do not yield better results. With that, this paper introduces a simple yet powerful deep learning based solution for automated COVID-19 classification.

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