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Authors: Robert Brodin ; Palawat Busaranuvong and Chun-Kit Ngan

Affiliation: Data Science Program, Worcester Polytechnic Institute, Worcester, MA, U.S.A.

Keyword(s): Convolutional Neural Network, Skin Cancer Detection, Automatic Architecture Design, Dermoscopic Image.

Abstract: We enhance and customize the automatically evolving genetic-based CNN (AE-CNN) framework to develop an auto-designed CNN (AutoCNN) pipeline to dynamically generate an optimal CNN model to assist physicians in detecting multi-skin cancer diseases (MSCD) over dermatoscopic images. Specifically, the contributions of this work are three-fold: (1) integrate the pre-processing module into the existing AE-CNN framework to sanitize and diversify dermatoscopic images; (2) enhance the evaluation algorithm of the framework to improve the model selection process by using the k-fold cross-validation; and (3) conduct the experimental study to present the accuracy results that the CNN model constructed by AutoCNN outperforms the model by AE-CNN to detect and classify MSCD.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Brodin, R.; Busaranuvong, P. and Ngan, C. (2022). AutoCNN-MSCD: An Autodesigned CNN Framework for Detecting Multi-skin Cancer Diseases over Dermoscopic Images. In Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 4: VISAPP; ISBN 978-989-758-555-5; ISSN 2184-4321, SciTePress, pages 607-615. DOI: 10.5220/0010893400003124

@conference{visapp22,
author={Robert Brodin. and Palawat Busaranuvong. and Chun{-}Kit Ngan.},
title={AutoCNN-MSCD: An Autodesigned CNN Framework for Detecting Multi-skin Cancer Diseases over Dermoscopic Images},
booktitle={Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 4: VISAPP},
year={2022},
pages={607-615},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010893400003124},
isbn={978-989-758-555-5},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 4: VISAPP
TI - AutoCNN-MSCD: An Autodesigned CNN Framework for Detecting Multi-skin Cancer Diseases over Dermoscopic Images
SN - 978-989-758-555-5
IS - 2184-4321
AU - Brodin, R.
AU - Busaranuvong, P.
AU - Ngan, C.
PY - 2022
SP - 607
EP - 615
DO - 10.5220/0010893400003124
PB - SciTePress

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