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

Abstract

Background: COVID-19 diagnosis in symptomatic patientsis 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.

Genomic image representation of human coronavirus sequences for COVID-19 detection

“…Medical imaging scans are among the most effective tools for detecting different diseases (e.g., COVID-19), as the images produced by these scans are analyzed using artificial intelligence and deep learning approaches [10] , [11] , [12] . Using these approaches to build a computer-based diagnostic system helps in the early detection of COVID-19 [13] , [14] , [15] , thus reducing healthcare workers' stress.…”

“…Aswathy et al [13] developed a system to detect COVID-19 from computed tomography (CT) images using a pre-trained ResNet-50 convolution neural network (CNN) model. When the system's output was a COVID-19, they…”

“…The previously described classification systems based on medical imaging scans [13] , [14] , [15] are highly accurate and play a significant role in treating the COVID-19 pandemic. However, they have significant drawbacks, such as exposing the patient to a high radiation dose, which can cause serious health complications, especially in pregnant women.…”

Artificial Intelligence for Forecasting the Prevalence of COVID-19 Pandemic: An Overview

“…The most common reported AI approaches to detect COVID-19 infections based on X-ray or CT images are CNN models [ 81 , 82 , 83 , 84 ]. Aswathy et al [ 85 ] used two CNN models, namely ResNet-50 and DenseNet-201, to identify and assess the COVID-19 infection from CT images as well as the severity condition of the patient. They succeeded in developing a single architecture of the model that can be used to achieve both targets.…”

Covid-19 Severity Prediction Using Deep Learning Models – Comparative Study

“…The study discovered that a customised training technique can enhance network performance and generalisation on a patient cohort with uneven severity levels and high equipment and procedure instability. The study [7]…”