Multiclass Skin Disease Classification within Dermoscopic Images Using Deep Neural Networks-Reference-Cited by-同舟云学术

Multiclass Skin Disease Classification within Dermoscopic Images Using Deep Neural Networks

Published:2023-12-07 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 12th International Symposium on Information and Communication Technology
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Author:

Ho-Le Minh-Quan¹^ORCID,Vo-Trung Hoang-Hung¹^ORCID,Nguyen Phuc-Tan¹^ORCID,Nguyen Trong-Thuan¹^ORCID,Tran Minh-Triet²^ORCID

Affiliation:

1. Faculty of Information Technology, University of Science, VNU-HCM, Vietnam and Vietnam National University, Vietnam

2. Faculty of Information Technology and Software Engineering Lab, University of Science, VNU-HCM, Vietnam and Vietnam National University, Vietnam

Funder

University of Science, VNU-HCM

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3628797.3629001

Reference35 articles.

1. FCN-Based DenseNet Framework for Automated Detection and Classification of Skin Lesions in Dermoscopy Images

2. Multiclass Skin Lesion Classification Using Hybrid Deep Features Selection and Extreme Learning Machine

3. Abien Fred Agarap . 2017. An architecture combining convolutional neural network (CNN) and support vector machine (SVM) for image classification. arXiv preprint arXiv:1712.03541 ( 2017 ). Abien Fred Agarap. 2017. An architecture combining convolutional neural network (CNN) and support vector machine (SVM) for image classification. arXiv preprint arXiv:1712.03541 (2017).

4. An enhanced technique of skin cancer classification using deep convolutional neural network with transfer learning models

5. A two‐stream deep neural network‐based intelligent system for complex skin cancer types classification