EDNN-PCXR: Enhanced Pediatric Chest X-Ray Classification using Fine-Tuned Deep Neural Networks

Abstract

In today's world, the rapid progress of artificial intelligence (AI) and machine learning (ML) presents remarkable opportunities for developing innovative solutions to tackle various challenges within the healthcare sector. Deep learning (DL) has become a powerful tool in healthcare, transforming patient care and improving clinical support. It is increasingly utilized to identify critical features in medical images that go beyond what the human eye can naturally detect. Chest X-ray images are a widely used medical tool for detecting various health conditions. This covers pneumonia, lung cancer, and other issues such as tissue damage and bone fractures. Regardless of experience, for radiologists, accurately identifying diseases from X-ray images can be a strenuous task. Diagnosing pneumonia, a viral lung infection, is especially difficult because its symptoms closely resemble those of other pulmonary diseases. This similarity reduces the accuracy of current diagnostic methods. The vast amount of information contained in X-ray images has created an increasing demand for computerized support systems. This paper compares various computer-aided pneumonia identification methods, incorporating different deep learning approaches to streamline diagnosis using images of chest X-rays. In this study, seven types of deep convolutional neural networks have been applied to a dataset containing 5,856 Chest X-ray images of normal and pneumonia cases. It has been observed that VGG-16, VGG-19, and ResNet-50 effectively classify images of Chest X-ray into normal and pneumonia affected cases. Among these architectures, VGG-16 performs the best with an accuracy of 91%, followed by VGG-19 at 90.38% and ResNet-50 at 89.94%. The results surpass those of the advanced techniques mentioned in the literature.