An efficient attention Densenet with LSTM for lung disease detection and classification using X-ray images supported by adaptive R2-Unet-based image segmentation.
Authors
Affiliations (5)
Affiliations (5)
- Department of ECE & CSE, Vignan's Institute of Engineering for Women, Kapujaggrajupeta, Visakhapatnam, India.
- Department of CSE, Annamalai University, Chidambaram, Tamil Nadu, India.
- School of Computer Science and Engineering, Vellore Institute of Technology, VIT-AP University, Amaravathi, India.
- EECE Department, GITAM (Deemed to be University), Visakhapatnam, Andhra Pradesh, India.
- Department of IT, Vignan's Institute of Engineering for Women, Kapujaggrajupeta, Visakhapatnam, India.
Abstract
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. The lung regions are segmented using an Adaptive Recurrent Residual U-Net (AR2-UNet), whose parameters are optimised using Enhanced Pufferfish Optimisation Algorithm (EPOA) to enhance segmentation accuracy. The segmented images are processed using "Attention-based Densenet with Long Short Term Memory(ADNet-LSTM)" for robust categorisation. Investigational results demonstrate that the proposed model achieves the highest classification accuracy of 93.92%, significantly outperforming several baseline models including ResNet with 90.77%, Inception with 89.55%, DenseNet with 89.66%, and "Long Short Term Memory (LSTM)" with 91.79%. Thus, the proposed framework offers a dependable and efficient solution for lung disease detection, supporting clinicians in early and accurate diagnosis.