Enhancing Lung Disease Classification through Deep Learning: A Comprehensive Study and Model Evaluation

This research tackles the pressing need for precise lung disease classification using deep learning models on chest X-ray images. Lung ailments pose a global health challenge, demanding accurate diagnostic tools to ease healthcare burdens and enhance patient outcomes. Traditional methods, often reliant on subjective human judgment, introduce potential errors and inefficiencies. The study centers on employing advanced deep learning models like DenseNet, MobileNet, ResNet, and ChexNet to automate classifying various lung diseases. Recognizing the complexities and variations in pulmonary conditions, the research aims to elevate diagnostic accuracy. It contributes to ongoing efforts to refine lung disease classification methods and bridge existing accuracy gaps. Through a comprehensive exploration of diverse deep learning models and their modifications, the study demonstrates improvements in model performance, indicating the potential for tailored modifications to address the intricacies of different lung diseases. The paper discusses the significance of the study in advancing automated diagnostic tools, providing insights into the relationships between specific diseases and deep learning models. The emphasis is on the adaptability and nuanced approach required for robust lung disease classification.