The DOI matches its Crossref record; 48 of the 48 references it cites were found in a registry or an index.
Deep neural network correlation learning mechanism for CT brain tumor detection
Open accessAbstract
Modern medical clinics support medical examinations with computer systems which use Computational Intelligence on the way to detect potential health problems in more efficient way. One of the most important applications is evaluation of CT brain scans, where the most precise results come from deep learning approaches. In this article, we propose a novel correlation learning mechanism (CLM) for deep neural network architectures that combines convolutional neural network (CNN) with classic architecture. The support neural network helps CNN to find the most adequate filers for pooling and convolution layers. As a result, the main neural classifier learns faster and reaches higher efficiency. Results show that our CLM model is able to reach about 96% accuracy, and about 95% precision and recall. We have described our proposed mechanism and discussed numerical results to draw conclusions and show future works.
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Azevedo, R. S. S., de Sousa, J. R., Araujo, M. T. F., Martins Filho, A. J., de Alcantara, B. N., Araujo, F. M. C., Queiroz, M. G. L., Cruz, A. C. R., Vasconcelos, B. H. B., Chiang, J. O., et al. (2018) · Scientific Reports · doi:10.1038/s41598-017-17765-5 · Cited by 3,478
Brain tumor classification using deep CNN features via transfer learning
Deepak, S., & Ameer, P. M. (2019) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2019.103345 · Cited by 1,321
Deep learning algorithms for detection of critical findings in head CT scans: a retrospective study
Chilamkurthy, S., Ghosh, R., Tanamala, S., Biviji, M., Campeau, N. G., Venugopal, V. K., Mahajan, V., Rao, P., & Warier, P. (2018) · The Lancet · doi:10.1016/s0140-6736(18)31645-3 · Cited by 1,020
Internet of Things and Big Data Analytics for Smart and Connected Communities
Sun, Y., Song, H., Jara, A. J., & Bie, R. (2016) · IEEE Access · doi:10.1109/access.2016.2529723 · Cited by 891
Enhanced Performance of Brain Tumor Classification via Tumor Region Augmentation and Partition
Cheng, J., Huang, W., Cao, S., Yang, R., Yang, W., Yun, Z., Wang, Z., & Feng, Q. (2015) · PLOS ONE · doi:10.1371/journal.pone.0140381 · Cited by 879
SG-One: Similarity Guidance Network for One-Shot Semantic Segmentation
Zhang, X., Wei, Y., Yang, Y., & Huang, T. S. (2020) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2020.2992433 · Cited by 570
Optimal deep learning model for classification of lung cancer on CT images
S.K., L., Mohanty, S. N., K., S., N., A., & Ramirez, G. (2019) · Future Generation Computer Systems · doi:10.1016/j.future.2018.10.009 · Cited by 540
Deep learning for image-based cancer detection and diagnosis − A survey
Hu, Z., Tang, J., Wang, Z., Zhang, K., Zhang, L., & Sun, Q. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2018.05.014 · Cited by 509
Xie, Y., Xia, Y., Zhang, J., Song, Y., Feng, D., Fulham, M., & Cai, W. (2019) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2018.2876510 · Cited by 496
Bhandary, A., Prabhu, G. A., Rajinikanth, V., Thanaraj, K. P., Satapathy, S. C., Robbins, D. E., Shasky, C., Zhang, Y.-D., Tavares, J. M. R. S., & Raja, N. S. M. (2020) · Pattern Recognition Letters · doi:10.1016/j.patrec.2019.11.013 · Cited by 449
Cited by
Raza, M. L., Hassan, S. T., Jamil, S., Hyder, N., Batool, K., Walji, S., & Abbas, M. K. (2025) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2025.1557177 · Cited by 74
Enhancing brain tumor detection in MRI images using YOLO-NeuroBoost model
Chen, A., Lin, D., & Gao, Q. (2024) · Frontiers in Neurology · doi:10.3389/fneur.2024.1445882 · Cited by 35
Metadata
- Authors
- Woźniak, M., Siłka, J., & Wieczorek, M.
- Venue
- Neural Computing and Applications
- Published
- 2021-03-16
- Volume
- 35
- Issue
- 20
- Pages
- 14611-14626
- ISSN
- 0941-0643 (ISSN-L)
- 1433-3058
- OpenAlex
- W3136070377
- Cited by
- 284
- References
- 48
- FWCI
- 15.56
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Woźniak, M., Siłka, J., & Wieczorek, M. (2023). Deep neural network correlation learning mechanism for CT brain tumor detection. Neural Computing and Applications. https://doi.org/10.1007/s00521-021-05841-x
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
