Detection of Pneumonia Cases from X-ray Chest Images using Deep Learning Based on Transfer Learning CNN and Hyperparameter Optimization

Abstract

Pneumonia is a viral infection affecting many people, especially in underdeveloped and impoverished nations where contaminated, crowded, and unhygienic living conditions are common and inadequate healthcare infrastructures. Recognizing pneumonia immediately is a challenging step that can increase survival odds and allow for early-stage treatment. The successful construction of prediction models makes use of the artificial intelligence discipline of deep learning. There are many approaches to identifying pneumonia, including CT scans, pulse oximetry, and many others, but X-ray tomography is the most popular method. However, reviewing chest X-rays (CXR) is difficult and vulnerable to subjectivity variations. Using x-ray chest images, this study suggests a novel deep learning-based architecture for the quick diagnosis of covid-19 and pneumonia cases. As our basic model, we use the CNN transfer learning models VGG16, ResNet50, and InceptionV3. To adjust the hyperparameters of our model, we use random search optimization approach.