Pneumonia Detection using Convolutional Neural Network

Abstract: The deadly illness known as pneumonia develops in the lungs and is brought on by a bacterial or viral infection. Pneumonia can be difficult and prone to error to diagnose in chest X-ray pictures due to its similarities to other lung infections. The purpose of this project is to create a computer-aided pneumonia detection system to speed up the process of making a diagnosis. As a result, an ensemble convolutional neural network (CNN) technique was presented for the automated diagnosis of pediatric pneumonia. In this case, the chest X-ray dataset was used to train seven well-known CNN models (VGG-16, VGG-19, ResNet-50, Inception-V3, Xception, MobileNet, and SqueezeNet) with the proper transfer learning and fine-tuning techniques after they had been pre-trained on the ImageNet dataset. The three best-performing models out of the seven were chosen for the ensemble approach. During the test, the ensemble method was used to combine the predictions made by CNN models to get the final findings. Furthermore, a CNN model was trained from the beginning, and its outcomes were contrasted with those of the suggested ensemble approach.