Balanced Convolutional Neural Networks for Pneumoconiosis Detection

Chaofan Hao, Nan Jin, Cuijuan Qiu, Kun Ba, Xiaoxi Wang, Huadong Zhang, Qi Zhao and Biqing Huang
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Chaofan Hao: Department of Automation, Tsinghua University, Beijing 100084, China
Nan Jin: Chongqing Center for Disease Control and Prevention, Department of Occupational Health and Radiation Health, Chongqing 400042, China
Cuijuan Qiu: Chongqing Center for Disease Control and Prevention, Department of Occupational Health and Radiation Health, Chongqing 400042, China
Kun Ba: Department of Automation, Tsinghua University, Beijing 100084, China
Xiaoxi Wang: Chongqing Center for Disease Control and Prevention, Department of Occupational Health and Radiation Health, Chongqing 400042, China
Huadong Zhang: Chongqing Center for Disease Control and Prevention, Department of Occupational Health and Radiation Health, Chongqing 400042, China
Qi Zhao: Chongqing Center for Disease Control and Prevention, Department of Occupational Health and Radiation Health, Chongqing 400042, China
Biqing Huang: Department of Automation, Tsinghua University, Beijing 100084, China

IJERPH, 2021, vol. 18, issue 17, 1-14

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.

Keywords: convolutional neural networks; pneumoconiosis detection; interpretability; balanced training (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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