Paediatric Frontal Chest Radiograph Screening with Fine-Tuned Convolutional Neural Networks

Within developing countries, there is a realistic need for technologies that can assist medical practitioners in meeting the increasing demand for patient screening and monitoring. To this end, computer aided diagnosis (CAD) based approaches to chest radiograph screening can be utilised in areas where there is a high burden of diseases such as tuberculosis and pneumonia. In this work, we investigate the efficacy of a purely data-driven approach to chest radiograph classification through the use of fine-tuned convolutional neural networks (CNN). We use two popular CNN models that are pre-trained on a large natural image dataset and two distinct datasets containing paediatric and adult radiographs respectively. Evaluation is performed using a 5-fold cross-validation analysis at an image level. The promising results, with top AUC metrics of 0.87 and 0.84 for the respective datasets, along with several characteristics of our data-driven approach motivate for the use of fine-tuned CNN models within this application of CAD.

[1]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[2]  Ronald M. Summers,et al.  Holistic classification of CT attenuation patterns for interstitial lung diseases via deep convolutional neural networks , 2018, Comput. methods Biomech. Biomed. Eng. Imaging Vis..

[3]  Xiaogang Wang,et al.  Medical image classification with convolutional neural network , 2014, 2014 13th International Conference on Control Automation Robotics & Vision (ICARCV).

[4]  Nima Tajbakhsh,et al.  Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning? , 2016, IEEE Transactions on Medical Imaging.

[5]  Christine Mulligan,et al.  Effectiveness of pneumococcal conjugate vaccine against presumed bacterial pneumonia hospitalisation in HIV-uninfected South African children: a case–control study , 2015, Thorax.

[6]  Yoshua Bengio,et al.  Understanding the difficulty of training deep feedforward neural networks , 2010, AISTATS.

[7]  Li Fei-Fei,et al.  ImageNet: A large-scale hierarchical image database , 2009, CVPR.

[8]  Bram van Ginneken,et al.  Off-the-shelf convolutional neural network features for pulmonary nodule detection in computed tomography scans , 2015, 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI).

[9]  Guy Brusselle,et al.  Dysregulated fibulin-5 expression and elastogenesis in COPD lungs: pyromaniac or fire fighter? , 2014, Thorax.

[10]  Xiang Zhang,et al.  OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks , 2013, ICLR.

[11]  Hayit Greenspan,et al.  Chest pathology detection using deep learning with non-medical training , 2015, 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI).

[12]  Ginés García-Mateos,et al.  Detection of Normality/Pathology on Chest Radiographs using LBP , 2010, BIOINFORMATICS.

[13]  Bram van Ginneken,et al.  Automatic classification of pulmonary peri-fissural nodules in computed tomography using an ensemble of 2D views and a convolutional neural network out-of-the-box , 2015, Medical Image Anal..

[14]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[15]  Ronald M. Summers,et al.  Improving Computer-Aided Detection Using Convolutional Neural Networks and Random View Aggregation , 2015, IEEE Transactions on Medical Imaging.

[16]  J. Carlin,et al.  Standardized interpretation of paediatric chest radiographs for the diagnosis of pneumonia in epidemiological studies. , 2005, Bulletin of the World Health Organization.

[17]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[18]  Nitish Srivastava,et al.  Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..

[19]  Stefan Carlsson,et al.  CNN Features Off-the-Shelf: An Astounding Baseline for Recognition , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops.

[20]  Michael Blum,et al.  High-Throughput Classification of Radiographs Using Deep Convolutional Neural Networks , 2016, Journal of Digital Imaging.

[21]  Clement J. McDonald,et al.  Preparing a collection of radiology examinations for distribution and retrieval , 2015, J. Am. Medical Informatics Assoc..

[22]  Martín Abadi,et al.  TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems , 2016, ArXiv.

[23]  Bram van Ginneken,et al.  Automatic detection of pleural effusion in chest radiographs , 2016, Medical Image Anal..

[24]  Elena Casiraghi,et al.  A Fully Automated Method for Lung Nodule Detection From Postero-Anterior Chest Radiographs , 2006, IEEE Transactions on Medical Imaging.

[25]  Mark D Cicero,et al.  Training and Validating a Deep Convolutional Neural Network for Computer-Aided Detection and Classification of Abnormalities on Frontal Chest Radiographs , 2017, Investigative radiology.

[26]  Yoshua Bengio,et al.  How transferable are features in deep neural networks? , 2014, NIPS.

[27]  Ronald M. Summers,et al.  Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning , 2016, IEEE Transactions on Medical Imaging.

[28]  Ronald M. Summers,et al.  Learning to Read Chest X-Rays: Recurrent Neural Cascade Model for Automated Image Annotation , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[29]  Thomas Brox,et al.  Striving for Simplicity: The All Convolutional Net , 2014, ICLR.