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Chest radiography is a technique based on medical imaging that is employed to detect thoracic diseases. In this paper, we designed an intelligent method to diagnose thorax disease from chest X-ray (CXR) images. A novel Empirical Curvelet Transform, coupled with a deep learning model, is proposed. The collected images are analysed using the proposed Empirical Curvelet Transform (ECT) model. Then, the outputs of ECT model are sent to DenseNet. The proposed model is assessed using several statistical metrics. The proposed model achieves an accuracy of 98%. The results demonstrated the ability of the proposed model to detect thoracic disease.

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ECT-DLM: Deep Learning-Based Empirical Curvelet Transform Approach for Thoracic Disease Diagnosis from X-RAY Images

  • 会议论文

  • 发表日期：

    2026年

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  • Sarmad K. D. Alkhafaji,
  • Shahab Abdulla,
  • Haydar Abdulameer Marhoon,
  • Mohammed Diykh,
  • Mustafa Ali Majed,
  • Jafar Sadiq,
  • Ali Aqeel Saleh,
  • Aqeel Sahi,
  • Hussein Alabdally

摘要

Chest radiography is a technique based on medical imaging that is employed to detect thoracic diseases. In this paper, we designed an intelligent method to diagnose thorax disease from chest X-ray (CXR) images. A novel Empirical Curvelet Transform, coupled with a deep learning model, is proposed. The collected images are analysed using the proposed Empirical Curvelet Transform (ECT) model. Then, the outputs of ECT model are sent to DenseNet. The proposed model is assessed using several statistical metrics. The proposed model achieves an accuracy of 98%. The results demonstrated the ability of the proposed model to detect thoracic disease.