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. 2021 Oct;14(10):1435-1445.
doi: 10.1016/j.jiph.2021.07.015. Epub 2021 Jul 29.

COVID-19 diagnosis and severity detection from CT-images using transfer learning and back propagation neural network

Aswathy A L  1 , Anand Hareendran S  2 , Vinod Chandra S S  3
Affiliations

COVID-19 diagnosis and severity detection from CT-images using transfer learning and back propagation neural network

Aswathy A L et al. J Infect Public Health. 2021 Oct.

Abstract

Background: COVID-19 diagnosis in symptomatic patients is an important factor for arranging the necessary lifesaving facilities like ICU care and ventilator support. For this purpose, we designed a computer-aided diagnosis and severity detection method by using transfer learning and a back propagation neural network.

Method: To increase the learning capability, we used data augmentation. Most of the previously done works in this area concentrate on private datasets, but we used two publicly available datasets. The first section diagnose COVID-19 from the input CT image using the transfer learning of the pre-trained network ResNet-50. We used ResNet-50 and DenseNet-201 pre-trained networks for feature extraction and trained a back propagation neural network to classify it into High, Medium, and Low severity.

Results: The proposed method for COVID-19 diagnosis gave an accuracy of 98.5% compared with the state-of-the-art methods. The experimental evaluation shows that combining the ResNet-50 and DenseNet-201 features gave more accurate results with the test data. The proposed system for COVID-19 severity detection gave better average classification accuracy of 97.84% compared with the state-of-the-art methods. This enables medical practitioners to identify the resources and treatment plans correctly.

Conclusions: This work is useful in the medical field as a first-line severity risk detection that is helpful for medical personnel to plan patient care and assess the need for ICU facilities and ventilator support. A computer-aided system that is helpful to make a care plan for the huge amount of patient inflow each day is sure to be an asset in these turbulent times.

Keywords: COVID-19; Computed tomography; DenseNet-201; Neural network; ResNet-50; Transfer learning.

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Figures

Fig. 1
Fig. 1
Examples of CT COVID-19 images (positive cases top row) (negative cases bottom row) from the SARS-CoV-2 CT-scan dataset.
Fig. 2
Fig. 2
Examples of CT COVID-19 images (positive cases top row) (negative cases bottom row) from the COVID-CT dataset.
Fig. 3
Fig. 3
Augmentation used by the proposed method.
Fig. 4
Fig. 4
Architecture of the proposed system.
Fig. 5
Fig. 5
Architecture for the COVID-19 diagnosis system.
Fig. 6
Fig. 6
Architecture for the COVID-19 severity detection system.
Fig. 7
Fig. 7
Convergence graph of accuracy and loss function using ResNet-50.
Fig. 8
Fig. 8
Confusion matrix for the COVID-19 diagnosis system.
Fig. 9
Fig. 9
ROC for the COVID-19 diagnosis system.
Fig. 10
Fig. 10
Confusion matrix for the COVID-19 severity detection (High, Medium, Low).
Fig. 11
Fig. 11
ROC for the COVID-19 severity detection (High, Medium, Low).
Fig. 12
Fig. 12
Validation performance of the back propagation neural network.
See this image and copyright information in PMC

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