Analysis of COVID-19 Infections on a CT Image Using DeepSense Model

Abstract

In this paper, a data mining model on a hybrid deep learning framework is designed to diagnose the medical conditions of patients infected with the coronavirus disease 2019 (COVID-19) virus. The hybrid deep learning model is designed as a combination of convolutional neural network (CNN) and recurrent neural network (RNN) and named as DeepSense method. It is designed as a series of layers to extract and classify the related features of COVID-19 infections from the lungs. The computerized tomography image is used as an input data, and hence, the classifier is designed to ease the process of classification on learning the multidimensional input data using the Expert Hidden layers. The validation of the model is conducted against the medical image datasets to predict the infections using deep learning classifiers. The results show that the DeepSense classifier offers accuracy in an improved manner than the conventional deep and machine learning classifiers. The proposed method is validated against three different datasets, where the training data are compared with 70%, 80%, and 90% training data. It specifically provides the quality of the diagnostic method adopted for the prediction of COVID-19 infections in a patient.

Predicting mortality rate and associated risks in COVID-19 patients

“…Still, it is established that Auto SARIMA can efficiently predict the cases of COVID-19 despite data deprivation. The researchers and healthcare professionals across the world have been continuously working hard to deal with this epidemic outbreak [31,32]. Moreover, the pandemic spread of such type of epidemic is also closely related to the social awareness as well as stringent policies put forth by the government.…”

“…Further, authors in [20] developed a classifier using data mining and hybrid deep learning named as deepsense classifier to classify the COVID-19 patients with respect to health conditions of lungs. The implementation of various ML forecasting models in COVID-19 is also simulated by authors in [21] by implementing different models like ARIMA, CUBIST, RF, RIDGE, SVR and the stackingensemble method for time series analysis and prediction.…”

A Deep Learning Method to Forecast COVID-19 Outbreak

“…Mathematically, an LSTM structure evaluates a mapping using an input sequence defined as x = ( x 1 , … , x T ) to an output sequence represented as y = ( y 1 , … , y T ) by computing the I run it activations iteratively through the following equations from t = 1 to T : where the W requisites determine weight matrices such as Wix is the weights matrix from the input gate to the input function and, W ic , W fc , W oc are diagonal matrices of weights for a peephole associations, further, the b provisos represents bias vectors where b i is the input gate bias vector, σ is the logistic sigmoid function, and I , f , o and c are respectively the input gate, f or get gate, output gate, and cell activation vectors all of which are of the same size as the cell output activation vector m , is the element-wise product of the vectors, g and h are the cell input and cell output activation functions [ 19 , 22 , 23 ].…”

Robotics and artificial intelligence in healthcare during COVID-19 pandemic: A systematic review

“…Similar to this, the deep learning-based COVID detection models have shown promising results and were adopted in many investigation [17] , [18] , [19] . CNN-based COVID detection models with DenseNet-201 architecture were presented in [20] , [21] , [22] . Several works proposed their own architecture for CT-image based COVID detection as in [23] designed the FaNet architecture which reported 94.83% accuracy, the COVNet model from [24] distinguished COVID cases with high specificity and sensitivity, [25] had manifested FGCNet which claimed to be better performing than 15 state-of-the-art methods.…”