Classification of distinct lung diseases using novel enhanced long short-term memory based optimization methodology

Lung disease classification using chest X-ray (CXR) images has become essential for early diagnosis and improved clinical decision-making. However, challenges such as low image quality, feature similarity among diseases, and classification instability reduce diagnostic reliability. To address these issues, this study proposes a novel ELSTM-AZOA framework for multiclass lung disease classification using the NIH CXR dataset. Initially, the collected CXR images are preprocessed using the balance contrast enhancement technique to improve image quality. U-Net + + is then employed for accurate lung region segmentation, followed by feature extraction using statistical and gray level co-occurrence matrix features. The extracted features are classified using an enhanced long short-term memory (ELSTM) network, while the American zebra optimization algorithm (AZOA) optimizes the model parameters to maximize classification accuracy. The proposed framework classifies six categories: healthy lung, tuberculosis, pneumonia, lung cancer, COPD, and COVID-19. Experimental results demonstrate that the proposed ELSTM-AZOA model achieves superior performance compared with existing methods, obtaining 6.36% higher accuracy and 6.43% higher precision. The findings confirm that the proposed framework provides robust, reliable, and promising computer-aided lung disease classification.