Transfer Learning-Based Lung Opacity Classification in Chest X-ray Image Dataset utilizing VGG16 pre-trained CNN Model

This research paper introduces a novel approach to lung opacity classification in chest X-ray images, leveraging transfer learning with a focus on Keras and the VGG16 model. In the context of the RSNA Pneumonia Detection Challenge dataset, our study delves into comprehensive data preprocessing steps, including dataset overview, splitting, and balancing. Notably, Keras is adapted for DICOM compatibility, addressing challenges in processing chest X-ray images. The application of simple data augmentation techniques enhances the dataset, contributing to the robustness of the developed model. The paper provides a detailed breakdown of the pretrained VGG16 model, outlining additional layers designed to optimize performance for the lung opacity classification task. Model evaluation encompasses a thorough analysis of architecture, parameters, and performance metrics such as precision, recall, and F1-score. The study concludes with insights into test-time augmentation strategies, emphasizing the importance of predicting each image twice for enhanced reliability. Overall, this research advances the field of medical imaging by demonstrating the efficacy of transfer learning in combination with Keras for accurate and efficient lung opacity classification in chest X-ray images.