A Novel Approach of CT Images Feature Analysis and Prediction to Screen for Corona Virus Disease (COVID-19)

Abstract

The paper demonstrates the analysis of Corona Virus Disease based on a probabilistic model. It involves a technique for classification and prediction by recognizing typical and diagnostically most important CT images features relating to Corona Virus. The main contributions of the research include predicting the probability of recurrences in no recurrence (first time detection) cases at applying our proposed approach for feature extraction. The combination of the conventional statistical and machine learning tools is applied for feature extraction from CT images through four images filters in combination with proposed composite hybrid feature extraction (CHFS). The selected features were classified by the stack hybrid classification system(SHC). Experimental study with real data demonstrates the feasibility and potential of the proposed approach for the said cause.

Overview of Coronavirus Disease and Imaging-Based Diagnostic Techniques

“…The program predicts the output after the application of 10-fold crossvalidation with an accuracy of 96.07% compared to 94.11% accuracy using CNN-based classification. Figure 16 shows the prediction steps of the proposed system with a hybrid classification mechanism (Farid et al 2020). To summarize, the proposed model performs better than the traditional classification methods because of the improved feature selection and enhancement of the classification mechanism and in addition to efficiently reduced wrong-negation rate (Farid et al 2020).…”

“…The approach to detect COVID-19 is similar to SARS on the frontlines; however, there is one big difference: the emergence of a powerful new weapon called artificial intelligence (AI) over the past couple of decades (Farid et al 2020;McCall 2020). Data mining plays a key role in biomedical sciences which allows predictions to identify and characterize pandemic with high accuracy (Farid et al 2020). Artificial intelligence (AI) can be defined as an ability of machines to understand and learn from new experiences and respond to new inputs, in addition to carrying out specific tasks in an autonomous manner.…”

“…The WHO suggests a similar approach for the current pandemic, but this time additionally, the WHO calls for the exploitation of intelligent computer programs which could be specifically designed and developed to check the current outbreak of SARS-CoV-2. These semiautonomous intelligent programs should be supplied with previously available knowledge available of the SARS epidemic, in addition to the abovementioned key factors about the novel coronavirus, thus ensuring that AI is linked with evidence information from a variety of sources including social media (Farid et al 2020;McCall 2020).…”

“…(1) Prediction of the effect of seasonal influence on COVID-19 occurence: The AI-based algorithm predicts how the warmer weather will benefit the spread of the virus. (Farid et al 2020).…”

“…Deep learning (DL) algorithms permit the extraction of detailed information from CT images for coronavirus disease diagnosis as it provides deeper insight into the diagnosis. DL algorithms can derive graphical features from the patient's images, i.e., they can determine inconsistencies in a patient with positive corona lab report with no clinical symptoms and, thus, provide prepathogenic clinical examination (Farid et al 2020;Zhu et al 2020a;Li et al 2020b;Cohen and Normile 2020). In the following paragraphs, some studies explaining the AI approaches have been detailed.…”