Authors:
Manu Goyal
1
;
Moi Hoon Yap
2
and
Saeed Hassanpour
3
Affiliations:
1
Department of Biomedical Data Science, Dartmouth College, Hanover, NH, U.S.A.
;
2
Visual Computing Lab, Manchester Metropolitan University, Manchester, U.K.
;
3
Departments of Biomedical Data Science, Computer Science and Epidemiology, Dartmouth College, Hanover, NH, U.S.A.
Keyword(s):
Skin Cancer, Fully Convolutional Networks, Multi-class Segmentation, Lesion Diagnosis.
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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