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<p>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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月24日

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  • A. Sundar Raj,
  • P. Anand Raj,
  • E. Dinesh,
  • Elangovan Muniyandy

摘要

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.