Abstract
Skin lesions are a severe disease globally. Early detection of melanoma in dermoscopy images significantly increases the survival rate. However, the accurate recognition of melanoma is extremely challenging due to the following reasons: low contrast between lesions and skin, visual similarity between melanoma and non-melanoma lesions, etc. Hence, reliable automatic detection of skin tumors is very useful to increase the accuracy and efficiency of pathologists. In this paper, we proposed two deep learning methods to address three main tasks emerging in the area of skin lesion image processing, i.e., lesion segmentation (task 1), lesion dermoscopic feature extraction (task 2) and lesion classification (task 3). A deep learning framework consisting of two fully convolutional residual networks (FCRN) is proposed to simultaneously produce the segmentation result and the coarse classification result. A lesion index calculation unit (LICU) is developed to refine the coarse classification results by calculating the distance heat-map. A straight-forward CNN is proposed for the dermoscopic feature extraction task. The proposed deep learning frameworks were evaluated on the ISIC 2017 dataset. Experimental results show the promising accuracies of our frameworks, i.e., 0.753 for task 1, 0.848 for task 2 and 0.912 for task 3 were achieved.
Highlights
Melanoma is the most deadly form of skin cancer and accounts for about 75% of deaths associated with skin cancer [1]
In International Skin Imaging Collaboration (ISIC) 2017, annotated datasets for three processing tasks related to skin lesion images, including lesion segmentation, dermoscopic feature extraction and lesion classification, were released for researchers to promote the accuracy of automatic melanoma detection methods
We proposed a framework consisting of multi-scale fully-convolutional residual networks and a lesion index calculation unit (LICU)
Summary
Melanoma is the most deadly form of skin cancer and accounts for about 75% of deaths associated with skin cancer [1]. Yu used a deep learning approach, i.e., a fully convolutional residual network (FCRN), for skin lesion segmentation in dermoscopy images [20]. In ISIC 2017, annotated datasets for three processing tasks related to skin lesion images, including lesion segmentation, dermoscopic feature extraction and lesion classification, were released for researchers to promote the accuracy of automatic melanoma detection methods. This work may become the benchmark for the following (3) We made detailed analysis of the proposed deep learning frameworks in several respects, e.g., related research in the area. The performances of networks with different depths; and the impact caused by adding different (3) We made detailed analysis of the proposed deep learning frameworks in several respects, e.g., the components (e.g., batch normalization, weighted softmax, etc.). This work provides useful guidelines for the design of deep learning networks in related medical research
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