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.
COVID-19 has spread worldwide and the World Health Organization was forced to list it as a Public Health Emergency of International Concern. The disease has severely impacted most of the people because it affects the lung and causes severe breathing problems and lung infections. Differentiating other lung ailments from COVID-19 infection and determining the severity is a challenging process. Doctors can give vital life-saving services and support patients' lives only if the severity of their condition is determined. This work proposed a two-step approach for detecting the COVID-19 infection from the lung CT images and determining the severity of the patient's illness. To extract the features, pre-trained models are used, and by analyzing them, integrated the features from AlexNet, DenseNet-201, and ResNet-50. The COVID-19 detection is carried out by using an Artificial Neural Network(ANN) model. After the COVID-19 infection has been identified, severity detection is performed. For that, image features are combined with the clinical data and is classified as High, Moderate, Low with the help of Cubic Support Vector Machine(SVM). By considering three severity levels, patients with high risk can be given more attention. The method was tested on a publicly available dataset and obtained an accuracy of 92.0%, sensitivity of 96.0%, and an F1-Score of 91.44% for COVID-19 detection and got overall accuracy of 90.0% for COVID-19 severity detection for three classes.
World Health Organization (WHO) declared COVID-19 (COronaVIrus Disease 2019) as pandemic on March 11, 2020. Ever since then, the virus is undergoing different mutations, with a high rate of dissemination. The diagnosis and prognosis of COVID-19 are critical in bringing the situation under control. COVID-19 virus replicates in the lungs after entering the upper respiratory system, causing pneumonia and mortality. Deep learning has a significant role in detecting infections from the Computed Tomography (CT). With the help of basic image processing techniques and deep learning, we have developed a two stage cascaded 3D UNet to segment the contaminated area from the lungs. The first 3D UNet extracts the lung parenchyma from the CT volume input after preprocessing and augmentation. Since the CT volume is small, we apply appropriate post-processing to the lung parenchyma and input these volumes into the second 3D UNet. The second 3D UNet extracts the infected 3D volumes. With this method, clinicians can input the complete CT volume of the patient and analyze the contaminated area without having to label the lung parenchyma for each new patient. For lung parenchyma segmentation, the proposed method obtained a sensitivity of 93.47%, specificity of 98.64%, an accuracy of 98.07%, and a dice score of 92.46%. We have achieved a sensitivity of 83.33%, a specificity of 99.84%, an accuracy of 99.20%, and a dice score of 82% for lung infection segmentation.
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