Multi-class Semantic Segmentation of Skin Lesions via Fully Convolutional Networks

Manu Goyal, Moi Yap, Saeed Hassanpour

Abstract

Melanoma is clinically difficult to distinguish from common benign skin lesions, particularly melanocytic naevus and seborrhoeic keratosis. The dermoscopic appearance of these lesions has huge intra-class variations and high inter-class visual similarities. Most current research is focusing on single-class segmentation irrespective of classes of skin lesions. In this work, we evaluate the performance of deep learning on multi-class segmentation of ISIC-2017 challenge dataset, which consists of 2,750 dermoscopic images. We propose an end-to-end solution using fully convolutional networks (FCNs) for multi-class semantic segmentation to automatically segment the melanoma, seborrhoeic keratosis and naevus. To improve the performance of FCNs, transfer learning and a hybrid loss function are used. We evaluate the performance of the deep learning segmentation methods for multi-class segmentation and lesion diagnosis (with post-processing method) on the testing set of the ISIC-2017 challenge dataset. The results showed that the two-tier level transfer learning FCN-8s achieved the overall best result with Dice score of 78.5% in a naevus category, 65.3% in melanoma, and 55.7% in seborrhoeic keratosis in multi-class segmentation and Accuracy of 84.62% for recognition of melanoma in lesion diagnosis.

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Paper Citation


in Harvard Style

Goyal M., Yap M. and Hassanpour S. (2020). Multi-class Semantic Segmentation of Skin Lesions via Fully Convolutional Networks.In Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3: C2C, ISBN 978-989-758-398-8, pages 290-295. DOI: 10.5220/0009380302900295


in Bibtex Style

@conference{c2c20,
author={Manu Goyal and Moi Yap and Saeed Hassanpour},
title={Multi-class Semantic Segmentation of Skin Lesions via Fully Convolutional Networks},
booktitle={Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3: C2C,},
year={2020},
pages={290-295},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009380302900295},
isbn={978-989-758-398-8},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3: C2C,
TI - Multi-class Semantic Segmentation of Skin Lesions via Fully Convolutional Networks
SN - 978-989-758-398-8
AU - Goyal M.
AU - Yap M.
AU - Hassanpour S.
PY - 2020
SP - 290
EP - 295
DO - 10.5220/0009380302900295