Utilizing Computer Vision, Clustering and Neural Networks for Melanoma Categorization
Abstract
References
Index Terms
- Utilizing Computer Vision, Clustering and Neural Networks for Melanoma Categorization
Recommendations
Automated malignant melanoma detection using MATLAB
DNCOCO'06: Proceedings of the 5th WSEAS international conference on Data networks, communications and computersMalignant melanoma, the most deadly form of skin cancer, has a good prognosis if treated in the curable early stages. Early diagnosis and surgical excision is the most effective treatment of melanoma. Well-trained dermatologists reach a high level of ...
Melanoma Risk Prediction with Structured Electronic Health Records
BCB '18: Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health InformaticsMelanoma is one of the fastest growing cancers in the world, and can affect patients earlier in life than most other cancers. Therefore, it is imperative to be able to identify patients at high risk for melanoma and enroll them in screening programs to ...
CNN-Based Deep Learning Model for Early Identification and Categorization of Melanoma Skin Cancer Using Medical Imaging
AbstractIn human life, skin cancer is a curse. If not appropriately diagnosed, it spreads around all body parts in the earlier stage. The melanoma skin cancer death rate is 75% all over the world. There is an urgent need for a cure to be in place. Swarm ...
Comments
Information & Contributors
Information
Published In
- Conference Chair:
- Morris Chang,
- Program Chair:
- Dan Lo,
- Publications Chair:
- Eric Gamess
Sponsors
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Check for updates
Author Tags
Qualifiers
- Poster
- Research
- Refereed limited
Conference
Acceptance Rates
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 63Total Downloads
- Downloads (Last 12 months)1
- Downloads (Last 6 weeks)0
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in