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Pneumoconiosis computer aided diagnosis system based on X-rays and deep learning.

Fan Yang^1,2, Zhi-Ri Tang^3,2, Jing Chen¹

¹Department of Radiology, The Affiliated Hospital of Southwest Medical University, Taiping Street, Luzhou, 646000, Sichuan, China.

BMC Medical Imaging

|December 9, 2021

Summary

This study developed a deep learning system for diagnosing pneumoconiosis from X-rays, achieving 92.46% accuracy. The AI demonstrates potential for medical diagnostics, though further refinement is needed for detailed classification.

Keywords:

Deep learning Pneumoconiosis diagnosis ResNet U-Net X-rays

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Area of Science:

Medical Imaging
Artificial Intelligence
Pulmonary Medicine

Background:

Pneumoconiosis diagnosis relies on imaging, but interpretation can be challenging.
Computer-aided diagnosis (CADx) systems offer potential for improved accuracy and efficiency.
Deep learning (DL) shows promise in medical image analysis.

Purpose of the Study:

To develop and evaluate a DL-based CADx system for detecting pneumoconiosis.
To differentiate between normal individuals and those with pneumoconiosis using X-ray images.
To assess the efficacy of a two-stage deep learning approach for lung disease classification.

Main Methods:

Utilized a dataset of 1760 anonymous digital X-ray images.
Implemented a two-stage deep learning pipeline: U-Net for lung segmentation, followed by ResNet-34 for classification.
Employed transfer learning to enhance feature extraction within the lung regions.

Main Results:

The DL system achieved an accuracy of 92.46% and an Area Under the Curve (AUC) of 89% in classifying pneumoconiosis.
The two-stage approach effectively focused feature extraction on lung regions, minimizing background interference.
Sub-classification into four categories resulted in a reduced accuracy of 70.1%.

Conclusions:

Deep learning is effective for pneumoconiosis diagnosis from X-rays, highlighting AI's potential in medical applications.
The proposed algorithm demonstrates strong performance for binary classification of pneumoconiosis.
Future research will explore CT imaging for more detailed lung region analysis and improved sub-classification accuracy.