Classification Covid-19 Based on X-Ray Using GLCM and ANN Backpropagation - Riyanto, Ghafur Ade (Jurnal Transformatika, 2022)
Coronavirus (Covid-19) is a disease that belongs to a large family of disorders that can cause mild to severe symptoms. The coronaviruses Middle East Respiratory Syndrome (MERS) and Serious Acute Respiratory Syndrome (SARS) are two types of coronaviruses that cause severe illness (SARS). According to WHO estimates as of December 17, 2021, Covid-19 has infected about 271,963,258 individuals, with a death rate of 5,331,019 cases. Hospitals only have a limited number of Covid-19 test kits because of the daily increase in cases. As a result, to prevent the spread of Covid-19 among persons, it is necessary to develop an automatic detection method as quickly as feasible, as well as other diagnosis options. The goal of this research was to employ GLCM to extract features and the Backpropagation Neural Network classification technique to automatically develop a Covid-19 diagnosis system by classifying the lungs into two groups: normal lungs and Covid-19 lungs. Pre-processing, segmentation, feature extraction, and classification were all used in this study's lung classification method. The accuracy of the test findings was assessed to be 85%. The sensitivity value for the normal class was 92.5%, whereas the sensitivity value for the Covid-19 class was 77.5%. The specification value for the normal class was 22.5%, whereas the specification value for the covid-19 class was 7.5%. It may be deduced from the accuracy, sensitivity, and specification percentages that the developed system is capable of categorizing lungs using X-Ray lung pictures.