A modified deep convolutional neural network for detecting COVID-19 and pneumonia from chest X-ray images based on the concatenation of Xception and ResNet50V2

By Mohammad Rahimzadeh, Abolfazl Attar. Informatics in Medicine Unlocked, 2020

540 citations recorded by the source corpus.

In this paper, we have trained several deep convolutional networks with introduced training techniques for classifying X-ray images into three classes: normal, pneumonia, and COVID-19, based on two open-source datasets. Our data contains 180 X-ray images that belong to persons infected with COVID-19, and we attempted to apply methods to achieve the best possible results. In this research, we introduce some training techniques that help the network learn better when we have an unbalanced dataset (fewer cases of COVID-19 along with more cases from other classes). We also propose a neural network that is a concatenation of the Xception and ResNet50V2 networks. This network achieved the best accuracy by utilizing multiple features extracted by two robust networks. For evaluating our network, we have tested it on 11302 images to report the actual accuracy achievable in real circumstances. The average accuracy of the proposed network for detecting COVID-19 cases is 99.50%, and the overall average accuracy for all classes is 91.4%.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/3600545236d4def5913e3462aef3df8858f81f3f

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In Artificial Intelligence

In Computer Vision and Pattern Recognition

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

A combined deep CNN-LSTM network for the detection of novel coronavirus (COVID-19) using X-ray images (2020, methodology): “[21] developed a concatenated CNN for classifying COVID-19 cases based on Xception and ResNet50V2 models using chest X-ray.”
CNN-based transfer learning–BiLSTM network: A novel approach for COVID-19 infection detection (2020, background): “…and Katarya [68] InstaCovNet-19 99.08 Sethy and Behera [69] ResNet50 + SVM 95.40 Hemdan, Shouman and Karar [70] VGG19 90.00 Rahimzadeh and Attar [71] Xception + ResNet50V2 91.40 Proposed Method Hybrid 98.70 and data augmentation steps is to give the result images as input to the designed deep…”