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. 2021 Feb 5;17(9):6480-6488.
doi: 10.1109/TII.2021.3057524. eCollection 2021 Sep.

COVID-19: Automatic Detection of the Novel Coronavirus Disease From CT Images Using an Optimized Convolutional Neural Network

Aniello Castiglione  1 , Pandi Vijayakumar  2 , Michele Nappi  3 , Saima Sadiq  4 , Muhammad Umer  4   5
Affiliations

COVID-19: Automatic Detection of the Novel Coronavirus Disease From CT Images Using an Optimized Convolutional Neural Network

Aniello Castiglione et al. IEEE Trans Industr Inform. .

Abstract

It is widely known that a quick disclosure of the COVID-19 can help to reduce its spread dramatically. Transcriptase polymerase chain reaction could be a more useful, rapid, and trustworthy technique for the evaluation and classification of the COVID-19 disease. Currently, a computerized method for classifying computed tomography (CT) images of chests can be crucial for speeding up the detection while the COVID-19 epidemic is rapidly spreading. In this article, the authors have proposed an optimized convolutional neural network model (ADECO-CNN) to divide infected and not infected patients. Furthermore, the ADECO-CNN approach is compared with pretrained convolutional neural network (CNN)-based VGG19, GoogleNet, and ResNet models. Extensive analysis proved that the ADECO-CNN-optimized CNN model can classify CT images with 99.99% accuracy, 99.96% sensitivity, 99.92% precision, and 99.97% specificity.

Keywords: COVID-19; Convolutional neural network (CNN); computed tomography (CT) images; deep learning; diagnostic imaging.

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Figures

Fig. 1.
Fig. 1.
ADECO-CNN architecture to detect the Coronavirus disease.
Fig. 2.
Fig. 2.
Number of patients in dataset. Among 60 COVID formula image patients, 32 are male and 28 are female patients, and in COVID formula image patients, 30 are male and 30 are female patients.
Fig. 3.
Fig. 3.
CT images of COVID formula image and COVID formula image cases. (a) COVID +ve Images. (b) COVID formula image Images.
Fig. 4.
Fig. 4.
Four steps of preprocessing. (a) CT scan images without preprocessing. (b) Detection of edges. (c) BGR-image to YUV-image conversion. (d) Equalization of image intensity. (e) YUV-image to BGR-image conversion.
Fig. 5.
Fig. 5.
Architectural diagram of the ADECO-CNN model.
Fig. 6.
Fig. 6.
ADECO-CNN model experimental evaluation. (a) Accuracy. (b) Loss. (c) Precision. (d) Recall (sensitivity).
Fig. 7.
Fig. 7.
Result comparison between ADECO-CNN and transfer learning models.
Fig. 8.
Fig. 8.
Stability of the ADECO-CNN approach with different training data ratio.
See this image and copyright information in PMC

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