Machine Learning-Based Classification of Chest X-Ray MRI Images into Covid-19 Graphic User Interface

Abstract

To predict the best performance metrics for the diagnostic pathology of Covid-19 based on MRI image feature extractions, primarily studies analysis are required to determine the optimal setting parameters such as the knn nearest-neighbors, the test size, and the random state. In this investigation, the performance metrics that tell us how much better a model is making prediction are presented. The system is implemented and simulated in Anaconda, and its performance is tested on a real dataset that contains six (06) features and two (02) classes. Each class, an abnormal class (a patient having Covid-19), and a normal class (a patient without Covid-19) consists of 343 instances (images), and 234 instances (images), respectively. At constant random state 66, the performance of test measurements obtained from the simulations results under various test sizes [10%~50%] is carried out when the nearest neighbor knn changes from 1 to 20. For quality analysis to examine and validate the proposed technique, based on the performance metrics, the simulation results achieved an average of train accuracy, test accuracy, precision score, sensitivity, F1-score, and specificity in the interval of (100.0±0.0~74.3±0.9)%, (82.9±3.4~71.6±2)%, (82.5±3.5~66.2±2.8)%, (82.0±6.4~60.1±4.1)%, (80.6±2.3~66.3±3.4)%, and (90.0± 2.3~71.8±3.4)%,respectively. The KNN classifier combined with the optimal setting parameters show better performance, predicting the normal and abnormal class labels accurately. Based on these results, we can further improve the accuracy performance in the range of k = [2~7] and the test size in the range of [10%~35%]. With these primarily studies analysis, we have developed a graphic user interface application to perform the diagnostic of pathology on Covid-19 disease that generates the optimal performance metrics.