Exploring Deep Learning for Detecting Lung Diseases

Lung diseases such as tuberculosis (TB), lung cancer, COVID-19, and pneumonia present major global health challenges. Traditional diagnostic methods, including chest X-rays and CT scans, face limitations related to cost, interpretation complexity, and radiation exposure. This research investigates the use of deep learning models for improving lung disease detection from chest X-rays, focusing on a novel approach with Deep Convolutional Generative Adversarial Networks (DCGANs) for data augmentation to address dataset imbalances. Four deep learning models—EfficientNet B0, DenseNet, CNN, and VGG16—were evaluated for their performance in detecting various lung diseases, with metrics including accuracy, precision, recall, and F1-score. The study found that EfficientNet B0 demonstrated superior performance, suggesting its potential to enhance diagnostic accuracy, support timely treatment, and reduce the burden on healthcare professionals. These findings offer valuable insights into early diagnosis and effective treatment strategies for lung diseases.