AI Integration in Medical Imaging: Advanced Analysis of Chest X-ray

In this research, we introduce two types of Artificial Intelligence (AI) models for classifying chest X-rays, binary and categorical. These models were trained and validated utilizing Convolutional Neural Network (CNNs) and transfer learning techniques. The binary classification model performed well in classifying normal and abnormal X-rays. The categorical classification model showed good abilities to recognize pathological states such as cardiomegaly and infiltration. However, it faced challenges when radiographic patterns overlapped. We used a dataset of 2,463 chest X-ray images with various pathological conditions and improved CNN architectures with two validation approaches to ensure robustness and reliability. This study contributes to the growing literature on AI in medical imaging, showing enhanced clinical outcomes with robust performance and predictive capabilities.

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