Implementation of Spatial-Level Augmentation on Pneumonia Classification with Convolutional Neural Network

Abstract

Pneumonia is a disease with symptoms of difficulty breathing, fever, dry cough, and chest pain. As an indication that a person has COVID-19, it is necessary to immediately identify pneumonia by a doctor whether the lung X-ray image of the patient is classified as pneumonia or not. The importance of early diagnosis can reduce the risk of death in patients. Convolutional Neural Network is one of the fields of study in the realm of Computer Science that can perform image-based object classification. CNN can be used to identify pneumonia based on thorax images based on the features or features displayed on the image. One of the important elements in CNN is the amount of data that can be used for training, validation, and testing. Generally, the more data entered, the more learning material for the CNN system so that the system can classify more accurately. This study aims to measure the accuracy of the CNN model on thoracic-pneumonia images with spatial level augmentation changes. Image augmentation is implemented to increase image variance with initial data of 5856 images. The applied augmentations are Affine, Flip, Pixel Dropout, Random Size Crop, and Shift Scale Rotate. The stages of this research are manually grouping images, implementing augmentation on images, applying training-validation-testing on CNN, and analyzing the output results of the developed system. By using 5 types of augmentation, the dataset used as learning material can be increased up to 5x the original amount. From the research carried out, it was found that the Random-sized Crop type augmentation gave the highest accuracy value of 94.719% or an increase of 3.808% from the non-augmentation testing data. From this research, it is hoped that studies related to augmentation can be a reference regarding the type of augmentation process and its results in finding the CNN accuracy value, especially in the case of pneumonia classification