The DOI matches its Crossref record; 45 of the 45 references it cites were found in a registry or an index.
Skin cancer diagnosis based on optimized convolutional neural network
Abstract
Early detection of skin cancer is very important and can prevent some skin cancers, such as focal cell carcinoma and melanoma. Although there are several reasons that have bad impacts on the detection precision. Recently, the utilization of image processing and machine vision in medical applications is increasing. In this paper, a new image processing based method has been proposed for the early detection of skin cancer. The method utilizes an optimal Convolutional neural network (CNN) for this purpose. In this paper, improved whale optimization algorithm is utilized for optimizing the CNN. For evaluation of the proposed method, it is compared with some different methods on two different datasets. Simulation results show that the proposed method has superiority toward the other compared methods.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Rethinking the Inception Architecture for Computer Vision
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.308 · Cited by 32,138
Goldberg, D. E., & Shakespeare, W. (2002) · Cited by 16,088
The Whale Optimization Algorithm
Mirjalili, S., & Lewis, A. (2016) · Advances in Engineering Software · doi:10.1016/j.advengsoft.2016.01.008 · Cited by 14,581
Dermatologist-level classification of skin cancer with deep neural networks
Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017) · Nature · doi:10.1038/nature21056 · Cited by 14,362
Byrd, A. L., Belkaid, Y., & Segre, J. A. (2018) · Nature Reviews Microbiology · doi:10.1038/nrmicro.2017.157 · Cited by 2,806
Acharya, U. R., Oh, S. L., Hagiwara, Y., Tan, J. H., & Adeli, H. (2018) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2017.09.017 · Cited by 1,647
Understanding Neural Networks Through Deep Visualization
Yosinski, J., Clune, J., Nguyen, A. M., Fuchs, T. J., & Lipson, H. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1506.06579 · Cited by 1,358
Cited by
Review of deep learning: concepts, CNN architectures, challenges, applications, future directions
Alzubaidi, L., Zhang, J., Humaidi, A. J., Al-Dujaili, A., Duan, Y., Al-Shamma, O., Santamaría, J., Fadhel, M. A., Al-Amidie, M., & Farhan, L. (2021) · Journal of Big Data · doi:10.1186/s40537-021-00444-8 · Cited by 7,906
Shetty, B., Fernandes, R., Rodrigues, A. P., Chengoden, R., Bhattacharya, S., & Lakshmanna, K. (2022) · Scientific Reports · doi:10.1038/s41598-022-22644-9 · Cited by 290
Naeem, A., Farooq, M. S., Khelifi, A., & Abid, A. (2020) · IEEE Access · doi:10.1109/access.2020.3001507 · Cited by 189
Accurate recognition of colorectal cancer with semi-supervised deep learning on pathological images
Yu, G., Sun, K., Xu, C., Shi, X.-H., Wu, C., Xie, T., Meng, R.-Q., Meng, X.-H., Wang, K.-S., Xiao, H.-M., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-26643-8 · Cited by 147
Melarkode, N., Srinivasan, K., Qaisar, S. M., & Plawiak, P. (2023) · Cancers · doi:10.3390/cancers15041183 · Cited by 144
Metadata
- Authors
- Zhang, N., Cai, Y.-X., Wang, Y.-Y., Tian, Y.-T., Wang, X.-L., & Badami, B.
- Venue
- Artificial Intelligence in Medicine
- Publisher
- Elsevier BV
- Published
- 2020-01
- Volume
- 102
- Pages
- 101756
- ISSN
- 0933-3657 (ISSN-L)
- OpenAlex
- W2986609444
- Cited by
- 312
- References
- 45
- FWCI
- 11.43
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Skin cancer diagnosis based on optimized convolutional neural network. https://app.cochat.ai/papers/10.1016/j.artmed.2019.101756
- The paper
- Zhang, N., Cai, Y.-X., Wang, Y.-Y., Tian, Y.-T., Wang, X.-L., & Badami, B. (2020). Skin cancer diagnosis based on optimized convolutional neural network. Artificial Intelligence in Medicine. https://doi.org/10.1016/j.artmed.2019.101756
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.
