Cited by 1,069COVID-19 diagnosis using AIAdvanced Neural Network ApplicationsRadiomics and Machine Learning in Medical Imaging

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Deep convolutional neural network based medical image classification for disease diagnosis

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Yadav, Samir S., Jadhav, Shivajirao M.·2019·Journal of Big Data

DOI 10.1186/s40537-019-0276-2

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Abstract

Abstract Medical image classification plays an essential role in clinical treatment and teaching tasks. However, the traditional method has reached its ceiling on performance. Moreover, by using them, much time and effort need to be spent on extracting and selecting classification features. The deep neural network is an emerging machine learning method that has proven its potential for different classification tasks. Notably, the convolutional neural network dominates with the best results on varying image classification tasks. However, medical image datasets are hard to collect because it needs a lot of professional expertise to label them. Therefore, this paper researches how to apply the convolutional neural network (CNN) based algorithm on a chest X-ray dataset to classify pneumonia. Three techniques are evaluated through experiments. These are linear support vector machine classifier with local rotation and orientation free features, transfer learning on two convolutional neural network models: Visual Geometry Group i.e., VGG16 and InceptionV3, and a capsule network training from scratch. Data augmentation is a data preprocessing method applied to all three methods. The results of the experiments show that data augmentation generally is an effective way for all three algorithms to improve performance. Also, Transfer learning is a more useful classification method on a small dataset compared to a support vector machine with oriented fast and rotated binary (ORB) robust independent elementary features and capsule network. In transfer learning, retraining specific features on a new target dataset is essential to improve performance. And, the second important factor is a proper network complexity that matches the scale of the dataset.

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ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380
Lecture Notes in Computer Science 1205
(1999) · Industrial Robot the international journal of robotics research and application · doi:10.1108/ir.1999.04926fae.001 · Cited by 39,228
ORB: An efficient alternative to SIFT or SURF
Rublee, E., Rabaud, V., Konolige, K., & Bradski, G. (2011) · 2011 International Conference on Computer Vision · doi:10.1109/iccv.2011.6126544 · Cited by 11,037
Deep convolutional neural networks for computer-aided detection: cnn architectures, dataset characteristics and transfer learning
Shin, H.-C., Roth, H. R., Gao, M., Lu, L., Xu, Z., Nogues, I., Yao, J., Mollura, D., & Summers, R. M. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2528162 · Cited by 5,759
Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning
Kermany, D. S., Goldbaum, M., Cai, W., Valentim, C. C. S., Liang, H., Baxter, S. L., McKeown, A., Yang, G., Wu, X., Yan, F., et al. (2018) · Cell · doi:10.1016/j.cell.2018.02.010 · Cited by 4,842
ChestX-Ray8: Hospital-Scale Chest X-Ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases
Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., & Summers, R. M. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.369 · Cited by 4,616
Dynamic Routing Between Capsules
Sabour, S., Frosst, N., & Hinton, G. E. (2017) · arXiv · doi:10.48550/arxiv.1710.09829 · Cited by 4,167
Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review
Rawat, W., & Wang, Z. (2017) · Neural Computation · doi:10.1162/neco_a_00990 · Cited by 3,503
GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classification
Frid-Adar, M., Diamant, I., Klang, E., Amitai, M. M., Goldberger, J., & Greenspan, H. K. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.09.013 · Cited by 2,055

Show all 30 references

Research works printed but not linked: 4

World Health Organization. Standardization of interpretation of chest radiographs for the diagnosis of pneumonia in children. Geneva: World Health Organization; 2001.
Center for Disease Control Prevention. Pneumonia can be prevented-vaccines can help. New York: Center for Disease Control Prevention; 2012.
Perez L, Wang J. The effectiveness of data augmentation in image classification using deep learning. 2017. arXiv preprint arXiv:1712.04621. · arXiv
Beutel J, Kundel H L, Van Metter R L, Fitzpatrick J M. Handbook of Medical Imaging: medical image processing and analysis, vol. 2. Bellingham: Spie Press; 2000.

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Authors: Yadav, S. S., & Jadhav, S. M.
Affiliations: Dr. Babasaheb Ambedkar Technological University (IN)
Corresponding author: Yadav, Samir S.
ORCID: Yadav, Samir S.
Jadhav, Shivajirao M.
Venue: Journal of Big Data
Publisher: Springer Science and Business Media LLC
Published: 2019-12
Volume: 6
Issue: 1
ISSN: 2196-1115 (ISSN-L)
DOI: 10.1186/s40537-019-0276-2
OpenAlex: W2995942064
Cited by: 1,069
References: 30
Citations by year: 2026: 57 · 2025: 139 · 2024: 234 · 2023: 215 · 2022: 197 · 2021: 166 · 2020: 61
FWCI: 69.98
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0 (tdm)
creativecommons.org/licenses/by/4.0 (vor, 16 d)
Locations: Journal Of Big Data (publishedVersion, cc-by)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics: Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › COVID-19 diagnosis using AI
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
Keywords: medical image classification, pneumonia detection, chest radiography, convolutional neural networks, transfer learning, VGG16, InceptionV3, capsule networks, support vector machine, ORB features, data augmentation, small medical image datasets

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Deep convolutional neural network based medical image classification for disease diagnosis

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