# Prediction of COVID-19 from Chest CT Images Using an Ensemble of Deep Learning Models
## 2 Related Work  
This section concerns some recent works related to the detection of COVID-19 from chest CT scans and X-rays. Reference [7] proposed the use of a DCNN-based approach on CT scans for differentiating between COVID-19 and typical viral pneumonia cases, achieving a 73% recognition accuracy. Reference [17] used a local-attention-based mechanism to distinguish between COVID, influenza, and healthy CT scans. Reference [33] proposed a network structure where DenseNet was used for feature extraction and proposed a DL model called DenseCapsNet to detect COVID-19 from a chest X-ray, achieving 98% accuracy. Reference [34] used a support vector machine (SVM) [35] to classify X-rays. Reference [36] used ML techniques for the extraction of graphical features from chest X-rays for COVID-19 detection. Several studies exist where the CT images are firstly segmented to highlight the ROI after which several strategies are employed to detect and classify them. Reference [37] proposed an approach to improve the segmentation of CT images using a modified U-Net architecture, which eliminates several drawbacks of the conventional U-Net architecture. Reference [38] used TL on DenseNet-121 to classify COVID-19. Their website takes radiology images, outputting the infected regions with an accuracy of 87%. Recently, [PERSON] et al. [39] released a public dataset consisting of CT scans collected from studies in this domain. They also tested the application of DenseNet to distinguish COVID-19 positive cases from the negatives with an accuracy of 84.7%. [PERSON] [40] developed a spiking neural network (SSN) by copying biological models, achieving a high \(F_{1}\) score. [PERSON] et al. [41] proposed a computationally economical method in which they extracted features of the CT scans, optimizing them by a clustering-based golden ratio optimizer (CGRO), and attained state-of-the-art accuracies on publicly available datasets. Authors in [42] proposed a SqueezeNet based model to distinguish COVID-19 CT images from other images and reported 85% sensitivity and a 0.8333 \(F_{1}\)-Score. Apart from using CT images, [PERSON] [43] explored a lung ultrasound (POCUS) dataset to show the importance and significance for COVID-19 detection. Their DL model (POCOVID-Net) was pre-trained on ImageNet to extract features from the images. They have reported a sensitivity of 0.96 and an \(F_{1}\)-Score of 0.92 using a 5-fold cross-validation. [PERSON] et al. [44] proposed a bi-stage framework for the recognition of COVID patients from chest CT-scan images. They extracted features from the DL-based models from which relevant features are selected using guided features selection methodology. These produced features are optimized using the Dragonfly algorithm producing a good recognition rate. [PERSON] et al. [45] introduced a synthetic chest X-ray generating model termed as auxiliary classifier generative adversarial Network (aCGaN). The images obtained are classified using custom-made DL-based models producing a decent accuracy. They also used the harmony search algorithm to optimize the produced features and retained the classification accuracy. [PERSON] et al. [46] used chest X-ray images for the efficient detection of COVID-19 using the VGG-19 model. Instead of directly predicting the class from the DL-based model, a feature extraction technique is employed. Extracted features from the VGG-19 model are fed into traditional machine learning models (logistic regression) to appropriately classify whether it is a COVID-positive or healthy patient. It achieved an accuracy of 99.26%. To evaluate the predictive power of DenseNet201 in COVID-19 identification using CT images, Reference [47] used pre-trained DenseNet201 on ImageNet for features extraction and modified dense layers to get the final output. Reference [48] used pre-trained U-Net for segmentation of 3D lung images. This helped them to predict the infected regions with an accuracy of 95.9%.