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

Research Open Access Automated chest screening based on a hybrid model of transfer learning and convolutional sparse denoising autoencoder Changmiao Wang1, 2, Ahmed Elazab3, 4, Fucang Jia1, Jianhuang Wu1 and Qingmao Hu1, 5Email author BioMedical Engineering OnLine201817:63 https://doi.org/10.1186/s12938-018-0496-2 ©  The Author(s) 2018 Received: 4 April 2018Accepted: 9 May 2018Published: 23 May 2018 Abstract Objective 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. Method 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. Results 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. Conclusion 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. Keywords Chest screeningComputer aided diagnosisDeep learningAutoencoderReceiver operating characteristic Background Chest screening is a basic procedure in radiology for lung disease prediction and diagnosis. However, such diagnosis is very time consuming and subjective. The turnaround time is of great importance in radiology as it is an important criterion to evaluate radiologists rather than the quality of their reports [1]. Especially in rural areas, direct care providers rely mainly on teleradiology for their chest X-rays (CXRs) interpretation. The emphasis on turnaround time can result in sub-standard reports, confusion, misdiagnosis, and gaps in communication with primary care physicians. All of these can severely and negatively impact patients’ care and may have life-changing consequences for patients. Our work is inspired by the recent progresses in image classification and segmentation. The former has substantially improved performance, largely due to the introduction of ImageNet database [2] and the advances in deep convolutional neural networks (CNNs) that effectively helps to recognize the images with a large pool of hierarchical representations. The CNNs can be used successfully in medical image classification and segmentation [3–6]. Many other techniques on lung disease detection and classification have been proposed [7–19]. Sufficient performance, no increase in reading time, seamless workflow integration, regulatory approval, and cost efficiency are the key points to the radiologists [20]. Tataru et al. applied three different neural network models for abnormality detection in CXR images that are not public released [21]. Our work will focus on classifying the CXRs as normal or abnormal in order to assist radiologists to move more quickly and efficiently. We then use the features extracted from deep neural network model for the classification of abnormalities in the CXRs. That is to say, we will classify the CXRs at three status levels: obvious abnormal, obvious normal, and uncertainty. Furthermore, a large collection of medical images can be automatically classified to three main levels, and the uncertainty status level images can be checked carefully by radiologist. A computer-aided triage system can mitigate these issues in several ways. Firstly, it will allow radiologists to focus their attention immediately on higher-risk cases. Secondly, it will provide radiologists with more information to help them correct potential misdiagnoses. The CXRs input to our algorithm will be a digital image format along with a label stating ‘normal’ or ‘abnormal’. In recent years, many methods were proposed for CXRs classification. There are many conventional machine learning methods to study the classification of X-ray chest radiographs [22, 23], such as based on texture and deformation features [24] or ensemble methods [25]. In addition, with the development of deep learning technology, many deep learning method are also applied to the field of medical image analysis. Yao et al. [26] explored the correlation among the 14 pathologic labels based on global images in Chest X-ray 14 [27] in order to classify CXRs, and rendering it as a multi label recognition problem. Using a variant of DenseNet [28] as an image encoder, they adopted the long-short term memory networks (LSTM) [29] so as to capture dependencies. Kumar et al. [30] investigated that which loss function is more suitable for training CNNs from scratch and presented a boosted cascaded CNN for global image classification. CheXNet [31] is the recent effective method for fine-tuning a 121-layer DenseNet on the global chest X-ray images, which has a modified last fully-connected layer. In this work, we demonstrate how to automatically classify CXRs at different status levels. Four publicly available radiology datasets that contain CXR images and reports namely are exploited as follows: the OpenI [17] open source literature, JSRT [32], Shenzhen Chest X-ray set [33], and NLM-Montgomery County Chest X-ray set [33]. A common challenge in medical image analysis is the data bias. When considering the whole population, diseased cases are much less than healthy cases, which is also the case in the CXRs datasets used. In our study, normal cases account for 75.9% (1883 images) of the entire dataset (2480 images), and the abnormal cases account for 24.1% (597 images) of the entire dataset. Inspired by the ideas introduced in [34, 35], we employ the already trained models to obtain the high level features and use them to infer the probability of image level. Then, we train an autoencoder network with the obtained joint image and generate new image based on decoder network to obtain the reconstruction (...truncated)