An efficient attention Densenet with LSTM for lung disease detection and classification using X-ray images supported by adaptive R2-Unet-based image segmentation.

Lung diseases represent one of the most prevalent health challenges globally, necessitating accurate diagnosis to improve patient outcomes. This work presents a novel deep learning-aided lung disease classification framework comprising three key phases: image acquisition, segmentation, and classification. Initially, chest X-ray images are taken from standard datasets.