Explanation of the Convolutional Neural Network Classifying Chest X-Ray Images Supporting Pneumonia Diagnosis

Medical images are valuable sources for disease diagnosis. Besides, advancements in deep learning in recent years have been supporting disease diagnosis methods based on images to obtain numerous achievements. However, deep learning algorithms still work as a black-box so it is difficult to interpret output from these algorithms. In this study, we propose a convolutional network architecture to classify Chest X-ray images as well as apply explanation approaches for trained models to support disease diagnosis. The proposed method provides insights in medical images to support Pneumonia diagnosis.

[1]  Shuqiang Li,et al.  Potential of deep learning in assessing pneumoconiosis depicted on digital chest radiography , 2020, Occupational and Environmental Medicine.

[2]  J. Kwon,et al.  An Algorithm Based on Deep Learning for Predicting In‐Hospital Cardiac Arrest , 2018, Journal of the American Heart Association.

[3]  Parisa Rashidi,et al.  Deep EHR: A Survey of Recent Advances in Deep Learning Techniques for Electronic Health Record (EHR) Analysis , 2017, IEEE Journal of Biomedical and Health Informatics.

[4]  Nataliya Sokolovska,et al.  Disease Prediction Using Synthetic Image Representations of Metagenomic Data and Convolutional Neural Networks , 2019, 2019 IEEE-RIVF International Conference on Computing and Communication Technologies (RIVF).

[5]  Hai Tang,et al.  Research on Medical Image Classification Based on Machine Learning , 2020, IEEE Access.

[6]  Yoram Singer,et al.  Adaptive Subgradient Methods for Online Learning and Stochastic Optimization , 2011, J. Mach. Learn. Res..

[7]  Hong Huang,et al.  A deep feature manifold embedding method for hyperspectral image classification , 2020 .

[8]  Chengcheng Yu,et al.  Imaging and clinical features of patients with 2019 novel coronavirus SARS-CoV-2 , 2020, European Journal of Nuclear Medicine and Molecular Imaging.

[9]  Emma J. Chory,et al.  A Deep Learning Approach to Antibiotic Discovery , 2020, Cell.

[10]  K. Lehnertz,et al.  Seizure prediction — ready for a new era , 2018, Nature Reviews Neurology.

[11]  Kuan-Lun Tseng,et al.  Joint Sequence Learning and Cross-Modality Convolution for 3D Biomedical Segmentation , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

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

[13]  Yicheng Fang,et al.  Sensitivity of Chest CT for COVID-19: Comparison to RT-PCR , 2020, Radiology.

[14]  Sebastian Thrun,et al.  Dermatologist-level classification of skin cancer with deep neural networks , 2017, Nature.

[15]  Ramandeep Singh,et al.  Deep learning in chest radiography: Detection of findings and presence of change , 2018, PloS one.

[16]  Rob Fergus,et al.  Visualizing and Understanding Convolutional Networks , 2013, ECCV.

[17]  Chenglong Yu,et al.  Research on Image Classification Method Based on DCNN , 2020, 2020 International Conference on Computer Engineering and Application (ICCEA).

[18]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[19]  Abhishek Das,et al.  Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization , 2016, 2017 IEEE International Conference on Computer Vision (ICCV).

[20]  Thanh Hai Nguyen,et al.  Metagenome-Based Disease Classification with Deep Learning and Visualizations Based on Self-organizing Maps , 2019, FDSE.

[21]  Michael H. Goldbaum,et al.  Labeled Optical Coherence Tomography (OCT) and Chest X-Ray Images for Classification , 2018 .