Abstract
Abstract: Pneumonia is one of the most deadly and dangerous lung infections. Pneumonia is a contagious, deadly disease in the lungs that is caused by bacteria, fungi, or viruses that invade the airways of a person's lungs with a load of fluid or pus. Chest Xray is a common method used to diagnose pneumonia and requires a medical professional to evaluate the outcome of the X-ray. For non-specialists, it is difficult to tell if a patient has pneumonia with chest X-ray images. When a convolutional neural network is used to handle this task, it will improve the diagnosis of pneumonia and reduce the workload of physicians. Medical Imaging has a great scope of application for the latest advances in computation. With emerging computer technology, the development of an automated pneumonia detection system is using Convolutional Neural Networks in line with different standardization (Dropout, L2, and Dropout + L2) and development methods (SGDM, RMSPROP and ADAM). Treatment for this disease is now especially possible, if the patient is in a remote area and medical services are limited. Therefore, the idea is to create an algorithm that automatically detects whether a patient has pneumonia or not by looking at chest X-ray images. Keywords: Convolutional Neural Network, Deep Learning, Image Recognition, prediction pneumonia
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