Abstract
Pneumoconiosis remains one of the most common and harmful occupational diseases in China, leading to huge economic losses to society with its high prevalence and costly treatment. Diagnosis of pneumoconiosis still strongly depends on the experience of radiologists, which affects rapid detection on large populations. Recent research focuses on computer-aided detection based on machine learning. These have achieved high accuracy, among which artificial neural network (ANN) shows excellent performance. However, due to imbalanced samples and lack of interpretability, wide utilization in clinical practice meets difficulty. To address these problems, we first establish a pneumoconiosis radiograph dataset, including both positive and negative samples. Second, deep convolutional diagnosis approaches are compared in pneumoconiosis detection, and a balanced training is adopted to promote recall. Comprehensive experiments conducted on this dataset demonstrate high accuracy (88.6%). Third, we explain diagnosis results by visualizing suspected opacities on pneumoconiosis radiographs, which could provide solid diagnostic reference for surgeons.
Highlights
We have proposed a pneumoconiosis radiograph dataset based on electronic health records provided by Chongqing CDC, China, which is a full image dataset under privacy protection guidelines
We improved the accuracy of pneumoconiosis detection by adding multiple layers within a dense block (DenseNet64) or tagging multiple dense blocks with the same inner structure (DenseNet53)
The diagnosis of pneumoconiosis is still largely dependent on the experience of radiologists, which affects the early diagnosis among huge populations
Summary
Pneumoconiosis is a disease caused by long-term inhalation of mineral dust [1]. According to the national occupational disease report, by the end of 2018, more than. 970,000 cases of occupational diseases were reported in China, and more than 870,000 cases of pneumoconiosis were included, accounting for about 90% of all occupational disease cases. Since 2010, the number of new pneumoconiosis cases reported each year has exceeded 20,000 cases. The average annual medical cost per pneumoconiosis case in China is 19.05 thousand yuan, and other indirect costs are 45.79 thousand yuan on average. Simplifying an average survival period after diagnosis as 32 years, the average economic burden caused by pneumoconiosis for each patient is 2.075 million yuan without taking inflation into account [2,3]
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