Automated chest screening based on a hybrid model of transfer learning and convolutional sparse denoising autoencoder

• Published in: Biomedical engineering online 2018-05, Vol.17 (1), p.63-63, Article 63
• Main Authors: Wang, Changmiao, Elazab, Ahmed, Jia, Fucang, Wu, Jianhuang, Hu, Qingmao
• Format: Article
• Language: English
• Subjects: Accuracy; Artificial intelligence; Autoencoder; Automation; Care and treatment; Chest; Chest screening; Chest x-rays; Classification; Computer aided diagnosis; Datasets; Deep learning; Diagnosis; Health screening; Humans; Image Processing, Computer-Assisted - methods; Information processing; International conferences; Lesions; Lung - diagnostic imaging; Lung diseases; Machine Learning; Medical imaging; Methods; Neural networks; Noise reduction; Pattern recognition; Radiography, Thoracic; Radiology; Receiver operating characteristic; Reconstruction; Regulatory approval; ROC Curve; Screening; Signal-To-Noise Ratio; Transfer learning; Triage - methods; X-rays
• ISSN: 1475-925X; 1475-925X
• DOI: 10.1186/s12938-018-0496-2

In this paper, we aim to investigate the effect of computer-aided triage system, which is implemented for the health checkup of lung lesions involving tens of thousands of chest X-rays (CXRs) that are required for diagnosis. Therefore, high accuracy of diagnosis by an automated system can reduce the radiologist's workload on scrutinizing the medical images. We present a deep learning model in order to efficiently detect abnormal levels or identify normal levels during mass chest screening so as to obtain the probability confidence of the CXRs. Moreover, a convolutional sparse denoising autoencoder is designed to compute the reconstruction error. We employ four publicly available radiology datasets pertaining to CXRs, analyze their reports, and utilize their images for mining the correct disease level of the CXRs that are to be submitted to a computer aided triaging system. Based on our approach, we vote for the final decision from multi-classifiers to determine which three levels of the images (i.e. normal, abnormal, and uncertain cases) that the CXRs fall into. We only deal with the grade diagnosis for physical examination and propose multiple new metric indices. Combining predictors for classification by using the area under a receiver operating characteristic curve, we observe that the final decision is related to the threshold from reconstruction error and the probability value. Our method achieves promising results in terms of precision of 98.7 and 94.3% based on the normal and abnormal cases, respectively. The results achieved by the proposed framework show superiority in classifying the disease level with high accuracy. This can potentially save the radiologists time and effort, so as to allow them to focus on higher-level risk CXRs.