Transfer learning based novel ensemble classifier for COVID-19 detection from chest CT-scans

Abstract

Coronavirus Disease 2019 (COVID-19) is a deadly infection that affects the respiratory organs in humans as well as animals. By 2020, this disease turned out to be a pandemic affecting millions of individuals across the globe. Conducting rapid tests for a large number of suspects preventing the spread of the virus has become a challenge. In the recent past, several deep learning based approaches have been developed for automating the process of detecting COVID-19 infection from Lung Computerized Tomography (CT) scan images. However, most of them rely on a single model prediction for the final decision which may or may not be accurate. In this paper, we propose a novel ensemble approach that aggregates the strength of multiple deep neural network architectures before arriving at the final decision. We use various pre-trained models such as VGG16, VGG19, InceptionV3, ResNet50, ResNet50V2, InceptionResNetV2, Xception, and MobileNet and fine-tune them using Lung CT Scan images. All these trained models are further used to create a strong ensemble classifier that makes the final prediction. Our experiments exhibit that the proposed ensemble approach is superior to existing ensemble approaches and set state-of-the-art results for detecting COVID-19 infection from lung CT scan images.

COVID-19 diagnosis via chest X-ray image classification based on multiscale class residual attention

“…Some literatures have reported that DNNs have achieved competitive performance in lung cancer [ 27 , 28 ], corneal endothelial cells [ 29 ], pancreas [ 30 , 31 ], polyp segmentation [ 32 , 33 ], etc. But, they are always prone to overfitting [ 34 , 35 ], poor robustness [ 36 ] and lack of generalization [ [37] , [38] , [39] ]. Furthermore, the lesion area of COVID-19 is usually small and blurred in CXR images, which needs us to pay more attention to these critical regions.…”

An automated diagnosis and classification of COVID-19 from chest CT images using a transfer learning-based convolutional neural network

“…The MTL achieved accuracies of 90.23% and 79.20% for detecting COVID-19 based on CT and RT-PCR. Shaik and Cherukuri [ 29 ] introduced a novel ensemble DNN strategy that used various TL-based pre-trained models for COVID-19 diagnosis based on CCT images. The strategy steps are as follows: preprocessing the CT images, feature extraction using the deep pre-trained models, fine-tuning the obtained features on a three-layered DNN, and classification via ensemble classifier.…”

A novel internet of things based on deep neural network framework using soft-attention convolutional neural networks for COVID-19 detection

“…Several DNN models have been developed for COVID-19 detection. In this respect, the CNN is the most frequently used method for COVID-19 ECG and CT images classification in comparison with other DNN-based models [12][13][14] [15][16] [17]. Concerning the classification of images, the proposed model is proved to be useful for detecting normal, COVID-19 and pneumonia cases.…”

“…The method reaches 87.5% of accuracy. ResNet50 is commonly used by researchers for feature extraction and classification of ECG and CT scan images [12][13][15] [16]. Metrics like accuracy, sensitivity, F1-score and specificity are essentially used for evaluating the performance of the classifier.…”