Prediction of Pneumonia COVID19 Using a Custom Convolutional Neural Network with Data Augmentation

COVID19 is a pandemic of infectious diseases caused by a coronavirus. This virus is a new variant found in Wuhan, China, in December 2019. Symptoms felt by COVID patients19, in general, are cold, the body feels tired, and dry cough. However, some patients may experience nasal congestion, runny nose, sore throat, or diarrhea. Medically, to identify this disease, visual radiological observation is carried out. The development of computer technology helps to process data through image processing. At this stage, the convolutional neural network is the latest and in-depth machine learning machine used to classify images. Observations did on X-Ray Images with four classes. Namely lung Normal condition 234 files, exposed to COVID 43 files, exposed to bacterial 242 files, and exposed to virus 148 files. Preprocessing did use auto contrast to improve image sharpness. Data augmentation was exposed to increase the amount of data variation. In addition to the X-Ray dataset, this research also uses two classes of COVID and NON-COVID on the CT-Scan dataset. The results were using 34-layers, resulting in an average accuracy of 99.25% and on 26-layer an average accuracy of 97.86%. The training time needed is 1 minute and 15 seconds. Average Error results for 34-layer is MSE 0.0237, RMSE 0.1441 and MAE 0.0120. It is 50% better than the 26 layer shows an average MAE of 0.00351.