Neighborhood Rough Neural Network Approach for COVID-19 Image Classification.

The rapid spread of the new Coronavirus, COVID-19, causes serious symptoms in humans and can lead to fatality. A COVID-19 infected person can experience a dry cough, muscle pain, headache, fever, sore throat, and mild to moderate respiratory illness, according to a clinical report. A chest X-ray (also known as radiography) or a chest CT scan are more effective imaging techniques for diagnosing lung cancer. Computed Tomography (CT) scan images allow for fast and precise COVID-19 screening. In this paper, a novel hybridized approach based on the Neighborhood Rough Set Classification method (NRSC) and Backpropagation Neural Network (BPN) is proposed to classify COVID and NON-COVID images. The proposed novel classification algorithm is compared with other existing benchmark approaches such as Neighborhood Rough Set, Backpropagation Neural Network, Decision Tree, Random Forest Classifier, Naive Bayes Classifier, K- Nearest Neighbor, and Support Vector Machine. Various classification accuracy measures are used to assess the efficacy of the classification algorithms.

Supervised hybrid feature selection based on PSO and rough sets for medical diagnosis.

Medical datasets are often classified by a large number of disease measurements and a relatively small number of patient records. All these measurements (features) are not important or irrelevant/noisy. These features may be especially harmful in the case of relatively small training sets, where this irrelevancy and redundancy is harder to evaluate. On the other hand, this extreme number of features carries the problem of memory usage in order to represent the dataset. Feature Selection (FS) is a solution that involves finding a subset of prominent features to improve predictive accuracy and to remove the redundant features. Thus, the learning model receives a concise structure without forfeiting the predictive accuracy built by using only the selected prominent features. Therefore, nowadays, FS is an essential part of knowledge discovery. In this study, new supervised feature selection methods based on hybridization of Particle Swarm Optimization (PSO), PSO based Relative Reduct (PSO-RR) and PSO based Quick Reduct (PSO-QR) are presented for the diseases diagnosis. The experimental result on several standard medical datasets proves the efficiency of the proposed technique as well as enhancements over the existing feature selection techniques.