Deep Feature Learning for Lung Disease and Lung Patches Detection from Chest CT and X-Ray Scans Using GANs

Accurate and efficient classification of lung diseases from chest CT and X-ray images is critical for timely diagnosis and treatment. This study proposes a novel hybrid framework that combines unsupervised representation learning via Generative Adversarial Networks (GANs) with supervised feature extraction using a pre-trained VGG16 convolutional neural network. The GAN component learns latent structural features from large amounts of unlabeled medical images, while VGG16 extracts high-level semantic features from labeled data. By fusing these complementary feature representations, the system achieves improved classification accuracy across multiple lung disease categories, including COVID-19, pneumonia, tuberculosis, and lung cancer. Experimental evaluations demonstrate the effectiveness of the proposed approach in leveraging both labeled and unlabeled data, reducing dependency on extensive manual annotations. The framework shows promising potential for scalable, automated lung disease diagnosis in clinical settings