Artificial convolution neural network techniques and applications for lung nodule detection
Abstract
We have developed a double-matching method and an artificial visual neural network technique for lung nodule detection. This neural network technique is generally applicable to the recognition of medical image pattern in gray scale imaging. The structure of the artificial neural net is a simplified network structure of human vision. The fundamental operation of the artificial neural network is local two-dimensional convolution rather than full connection with weighted multiplication. Weighting coefficients of the convolution kernels are formed by the neural network through backpropagated training. In addition, we modeled radiologists' reading procedures in order to instruct the artificial neural network to recognize the image patterns predefined and those of interest to experts in radiology. We have tested this method for lung nodule detection. The performance studies have shown the potential use of this technique in a clinical setting. This program first performed an initial nodule search with high sensitivity in detecting round objects using a sphere template double-matching technique. The artificial convolution neural network acted as a final classifier to determine whether the suspected image block contains a lung nodule. The total processing time for the automatic detection of lung nodules using both prescan and convolution neural network evaluation was about 15 seconds in a DEC Alpha workstation.
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A CNN was used for lung nodule detection in chest x-rays as early as 1995.
“One of the earliest applications of convolutional neural networks (CNN, the currently most common form of deep learning) has appeared as early as 1995, where a CNN was used for lung nodule detection in chest x-rays [100].”
From AI in Medical Imaging Informatics: Current Challenges and Future Directions (2020) · cites this paper as [100]
Cites
Learning Internal Representations by Error Propagation
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1985) · doi:10.21236/ada164453 · Cited by 16,501 · see citing works
Phoneme recognition using time-delay neural networks
Waibel, A., Hanazawa, T., Hinton, G., Shikano, K., & Lang, K. J. (1989) · IEEE Transactions on Acoustics, Speech, and Signal Processing · doi:10.1109/29.21701 · Cited by 2,643 · see citing works
Neocognitron: A neural network model for a mechanism of visual pattern recognition
Fukushima, K., Miyake, S., & Ito, T. (1983) · IEEE Transactions on Systems Man and Cybernetics · doi:10.1109/tsmc.1983.6313076 · Cited by 715 · see citing works
Handwritten digit recognition: applications of neural network chips and automatic learning
Le Cun, Y., Jackel, L. D., Boser, B., Denker, J. S., Graf, H. P., Guyon, I., Henderson, D., Howard, R. E., & Hubbard, W. (1989) · IEEE Communications Magazine · doi:10.1109/35.41400 · Cited by 480 · see citing works
Artificial convolution neural network for medical image pattern recognition
Lo, S. B., Chan, H., Lin, J.-S., Li, H., Freedman, M. T., & Mun, S. K. (1995) · Neural Networks · doi:10.1016/0893-6080(95)00061-5 · Cited by 321 · see citing works
Self-citationEffects of sample size in classifier design
Fukunaga, K., & Hayes, R. R. (1989) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.31448 · Cited by 320 · see citing works
Smart sensing within a pyramid vision machine
Burt, P. J. (1988) · Proceedings of the IEEE · doi:10.1109/5.5971 · Cited by 288 · see citing works
Giger, M. L., Doi, K., & MacMahon, H. (1988) · Medical Physics · doi:10.1118/1.596247 · Cited by 250 · see citing works
Handwritten alphanumeric character recognition by the neocognitron
Fukushima, K., & Wake, N. (1991) · IEEE Transactions on Neural Networks · doi:10.1109/72.97912 · Cited by 217 · see citing works
Zhang, W., Itoh, K., Tanida, J., & Ichioka, Y. (1990) · Applied Optics · doi:10.1364/ao.29.004790 · Cited by 209 · see citing works
Chan, H., Lo, S. B., Sahiner, B., et al. (1995) · Medical Physics · doi:10.1118/1.597428 · Cited by 208 · see citing works
Self-citationEvaluation of Diagnostic Systems
(1982) · Elsevier eBooks · doi:10.1016/b978-0-12-679080-1.x5001-4 · Cited by 165 · see citing works
Non-small-cell lung cancer: results of the New York screening program.
Heelan, R. T., Flehinger, B. J., Melamed, M. R., Zaman, M., Perchick, W. B., Caravelli, J. F., & Martini, N. (1984) · Radiology · doi:10.1148/radiology.151.2.6324279 · Cited by 152 · see citing works
Wu, Y. C., Doi, K., Giger, M. L., et al. (1992) · Medical Physics · doi:10.1118/1.596845 · Cited by 143 · see citing works
Shih-Chung, B. L., Freedman, M. T., Lin, J.-S., & Mun, S. K. (1993) · Journal of Digital Imaging · doi:10.1007/bf03168418 · Cited by 94 · see citing works
Self-citationGiger, M. L., Ahn, N., Doi, K., MacMahon, H., & Metz, C. E. (1990) · Medical Physics · doi:10.1118/1.596478 · Cited by 94 · see citing works
Value of the New TNM Staging System for Lung Cancer
Mountain, C. F. (1989) · CHEST Journal · doi:10.1378/chest.96.1_supplement.47s · Cited by 76 · see citing works
Computerized Search of Chest Radiographs for Nodules
Lampeter, W. A., & Wandtke, J. C. (1986) · Investigative Radiology · doi:10.1097/00004424-198605000-00003 · Cited by 43 · see citing works
Classification of patterns using a self-organizing neural network
Menon, M. M., & Heinemann, K. G. (1988) · Neural Networks · doi:10.1016/0893-6080(88)90026-3 · Cited by 33 · see citing works
Gómez-Morán, M. C. (1995) · PubMed · Cited by 29 · see citing works
FEASIBILITY OF COMPUTER-AIDED DIAGNOSIS IN DIGITAL RADIOGRAPHY
Doi, K., Katsuragawa, S., M.LGIGER, Fujita, H., & MacMahon, H. (1989) · Japanese Journal of Radiological Technology · doi:10.6009/jjrt.kj00001358902 · Cited by 11 · see citing works
Multiresolution scene segmentation using MLPs
Viennet, E., & Soulié, F. F. (2003) · 2022 International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.1992.227190 · Cited by 8 · see citing works
A self-learning visual pattern explorer and recognizer using a higher order neural network
Linhart, G., & Dorffner, G. (2003) · 2022 International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.1992.227069 · Cited by 6 · see citing works
Improving temporal representation in TDNN structure for phoneme recognition
Sivakumar, S. C., Robertson, W., & Maleod, K. (2003) · 2022 International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.1992.227232 · Cited by 5 · see citing works
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Cited by
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A survey on deep learning in medical image analysis
Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., van der Laak, J. A. W. M., van Ginneken, B., & Sánchez, C. I. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.07.005 · Cited by 15,332 · see citing works
Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?
Tajbakhsh, N., Shin, J. Y., Gurudu, S. R., Hurst, R. T., Kendall, C. B., Gotway, M. B., & Liang, J. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2535302 · Cited by 3,277 · see citing works
AI in Medical Imaging Informatics: Current Challenges and Future Directions
Panayides, A. S., Amini, A., Filipovic, N. D., Sharma, A., Tsaftaris, S. A., Young, A., Foran, D., Do, N., Golemati, S., Kurc, T., et al. (2020) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2020.2991043 · Cited by 713 · see citing works
NiftyNet: a deep-learning platform for medical imaging
Gibson, E., Li, W., Sudre, C., Fidon, L., Shakir, D. I., Wang, G., Eaton-Rosen, Z., Gray, R., Doel, T., Hu, Y., et al. (2018) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2018.01.025 · Cited by 552 · see citing works
Deep Convolutional Neural Networks Enable Discrimination of Heterogeneous Digital Pathology Images
Khosravi, P., Kazemi, E., Imielinski, M., Elemento, O., & Hajirasouliha, I. (2018) · EBioMedicine · doi:10.1016/j.ebiom.2017.12.026 · Cited by 334 · see citing works
Giger, M. L., Chan, H., & Boone, J. (2008) · Medical Physics · doi:10.1118/1.3013555 · Cited by 311 · see citing works
Celard, P., Iglesias, E. L., Sorribes-Fdez, J. M., Romero, R., Vieira, A. S., & Borrajo, L. (2023) · Neural Computing and Applications · doi:10.1007/s00521-022-07953-4 · Cited by 188 · see citing works
van Tulder, G., & de Bruijne, M. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2526687 · Cited by 142 · see citing works
- Authors
- Lo, S.-C. B., Lou, S.-L. A., Lin, J.-S., Freedman, M. T., Chien, M. V., & Mun, S. K.
- Affiliations
- Georgetown University (US)
- University of California, San Francisco (US)
- Georgetown University Medical Center (US)
- Venue
- IEEE Transactions on Medical Imaging
- Published
- 1995
- Volume
- 14
- Issue
- 4
- Pages
- 711-718
- ISSN
- 0278-0062 (ISSN-L)
- OpenAlex
- W2161113826
- Cited by
- 348
- References
- 24
- Citations by year
- 2026: 7 · 2025: 16 · 2024: 23 · 2023: 29 · 2022: 37 · 2021: 34 · 2020: 42 · 2019: 35 · 2018: 25 · 2017: 16 · 2016: 12 · 2015: 5 · 2014: 3 · 2013: 6 · 2012: 7
- FWCI
- 4.75
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Medical Imaging (publishedVersion)
- PubMed (publishedVersion)
- Funders
- University of Michigan
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Lung Cancer Diagnosis and Treatment
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Keywords
- lung nodule detection, convolutional neural networks, gray-scale imaging, computer-aided diagnosis, nodule classification
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