![Open Access](https://i-scholar.in/lib/pkp/templates/images/icons/fulltextgreen.png)
![Restricted Access](https://i-scholar.in/lib/pkp/templates/images/icons/fulltextred.png)
![Open Access](https://i-scholar.in/lib/pkp/templates/images/icons/fulltextgreen.png)
![Open Access](https://i-scholar.in/lib/pkp/templates/images/icons/fulltext_open_medium.gif)
![Restricted Access](https://i-scholar.in/lib/pkp/templates/images/icons/fulltextred.png)
![Restricted Access](https://i-scholar.in/lib/pkp/templates/images/icons/fulltext_restricted_medium.gif)
Cloud Based Solution for Skin Cancer Classification Using Machine Learning Models with Image Segmentation
Subscribe/Renew Journal
According to the research 30,000 people are affected by skin cancer per year. Skin cancer is the unusual growth of skin cells. It occurs on the skin area which is exposed to sunlight. It is classified into two types - Melanoma or Benign. In our proposed work, we use deep learning concept in order to perform segmentation and classification of the lesions. We make use of the full resolution Convolutional Network (FrCN) to segment the skin cancer image. Then the segmented image is given as input to a deep residual network for classification.
Keywords
No Keywords.
User
Subscription
Login to verify subscription
Font Size
Information
- Z. Yu, X. Jiang, F. Zhou, J. Qin, D. Ni, S. Chen, B. Lei, and T. Wang, “Melanoma Recognition in Dermoscopy Images via Aggregated Deep Convolutional Features,” IEEE Transactions on Biomedical Engineering, Aug 20, 2018.
- M. A. Al-Masni, M. A. Al-Antari, J. M. Park, G. Gi, T. Y. Kim, P. Rivera, E. Valarezo, S.-M. Han, and T.-S. Kim, “Detection and classification of the breast abnormalities” Jeju Island, Republic of Korea, 2017, pp. 1230-1233.
- M. Attia, M. Hossny, S. Nahavandi and A. Yazdabadi, "Skin melanoma segmentation using recurrent and convolutional neural networks," 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), 2017, pp. 292-296, doi: 10.1109/ISBI.2017.7950522.
- S. Aanjanadevi, V. Palanisamy and S. Aanjankumar, "An Improved Method for Generating Biometric- Cryptographic System from Face Feature," 2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI), 2019, pp. 1076-1079, doi: 10.1109/ICOEI.2019.8862741.
- T. Elsken, J. H. Metzen, and F. Hutter, ``Simple and efficient architecture search for convolutional neural networks,'' in Proc. 6th Int. Conf. Learn.Represent. (ICLR), Vancouver, BC, Canada, Apr./May 2018, pp
- E. Real, ``Large-scale evolution of image classifiers,'' in Proc. 34th Int.Conf. Mach. Learn. (ICML), Sydney, NSW, Australia, vol. 70, Aug. 2017,pp. 29022911.
- J. Bergstra and Y. Bengio, ``Random search for hyper-parameter optimization,''J. Mach. Learn. Res., vol. 13, pp. 281305, Feb. 2012.
- Lisheng Wei, Kun Ding, and Huosheng Hu, “Automatic Skin Cancer Detection in Dermoscopy Images Based on Ensemble Lightweight Deep Learning Network”2020
![](https://i-scholar.in/public/site/images/abstractview.png)
Abstract Views: 125
![](https://i-scholar.in/public/site/images/pdfview.png)
PDF Views: 0