Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.14365/3522
Title: | Deep learning based melanoma detection from dermoscopic images | Other Titles: | Dermoskopik görüntülerden derin ö?renme tabanli melanom tespiti | Authors: | Berkay M. Mergen E.H. Binici R.C. Bayhan Y. Gungor A. Okur E. Unay D. |
Keywords: | Convolutional neural networks Deep learning Dermoscopy Melanoma Skin cancer Biomedical engineering Deep learning Deep neural networks Diagnosis Diseases Electronic medical equipment Large dataset Neural networks Oncology Automatic Detection Convolutional neural network Dermoscopic images Dermoscopy Melanoma Melanoma detection Skin cancers Visual investigation Dermatology |
Publisher: | Institute of Electrical and Electronics Engineers Inc. | Abstract: | Melanoma which occurs with non-healing DNA degradation in melanocyte cells, is the most deadly type of skin cancers. Importantly, it can be identified for a treatment before it spreads to other tissues, i.e., early diagnosis. To identify, a specialist visually inspects whether the suspected lesion is melanoma or not. However, due to different education and experience levels of specialists or as a result of the patient not being in a facility that is specialized to this area, the problem of 'subjectivity' arises, and a good visual investigation accuracy may not always be achieved. Therefore, there is a significant need for automatic detection tools and systems. In this study, a method based on deep learning for automatic detection of melanoma from dermoscopic images is proposed. The developed system is tested with a large dataset and encouraging results are obtained. © 2019 IEEE. | Description: | 2019 Scientific Meeting on Electrical-Electronics and Biomedical Engineering and Computer Science, EBBT 2019 -- 24 April 2019 through 26 April 2019 -- 148870 | URI: | https://doi.org/10.1109/EBBT.2019.8741934 https://hdl.handle.net/20.500.14365/3522 |
ISBN: | 9.78173E+12 |
Appears in Collections: | Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection |
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