Pneumonia detection by deep learning models based on image processing method: A novel approach

Pneumonia is a common and challenging disease to treat. Diagnosis of pneumonia is performed by analysing chest X-ray images with a specialist doctor today. This situation can create an excessive workload for doctors and prolong the diagnosis time. Performing early and accurate diagnosis of pneumonia using pre-trained deep learning models, which are a subcategory of the deep learning method, can be extremely beneficial. Using computeraided diagnosis systems increases the accuracy of pneumonia diagnosis and thanks to these systems, doctors have an idea about the disease before diagnosis. In this study chest X-Ray images were classified as healthy or pneumonia using pre-trained deep learning methods. The histogram equalisation image processing method was used to improve image quality and the mask region-based convolutional neural network pre-trained method was used to segment the chest region. Alexnet, ResNet18 and VGG16 pre-trained models were used for image classification as healthy and pneumonia. ResNet18 showed outstanding performance in this (0.987), success rates were achieved by using the ResNet18 model. This study has shown that deep learning models can achieve high success rates in pneumonia diagnosis.