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. Skin lesion segmentation is the essential step for most classification approaches. Recent review of automated skin lesion segmentation algorithms can be found in [3] . Accurate segmentation can benefit the accuracy of subsequent lesion classification. Extensive studies [4] [5] [6] [7] [8] [9] [10] [11] [12] have been made to produce decent lesion segmentation results. For example, Gomez et al. proposed an unsupervised algorithm, named Independent Histogram Pursuit (IHP), for the segmentation of skin lesion [13] . The algorithm was tested on five different dermatological datasets, and achieved a competitive accuracy close to 97%. Zhou developed several mean-shift-based approaches for segmenting skin lesions in dermoscopic images [14] [15] [16] . Garnavi et al. proposed an automated segmentation approach for skin lesion using optimal color channels and hybrid thresholding technique [17] . In more recent research, Pennisi et al. employed Delaunay Triangulation to extract binary masks of skin lesion regions, which does not require any training stage [18] . Ma proposed a novel deformable model using a newly defined speed function and stopping criterion for skin lesion segmentation, which is robust against noise and yields effective and flexible segmentation performance [19] . Yu used a deep learning approach, i.e., a fully convolutional residual network (FCRN), for skin lesion segmentation in dermoscopy images [20] . Based on the segmentation results, hand-crafted features can be extracted for melanoma recognition. Celebi et al. extracted several features, including color and texture from segmented lesion region for skin lesion classification [21] . Schaefer used an automatic border detection approach [22] to segment the lesion area and then assembled the extracted features, i.e., shape, texture and color, for melanoma recognition [23] . On the other hand, some investigations [24] have attempted to directly employ hand-crafted features for melanoma recognition without a segmentation step. Different from approaches using hand-crafted features, deep learning networks use hierarchical structures to automatically extract features. Due to the breakthroughs made by deep learning in an increasing number of image-processing tasks [25] [26] [27] [28] , some research has started to apply deep learning approaches for melanoma recognition. Codella et al. proposed a hybrid approach, integrating convolutional neural network (CNN), sparse coding and support vector machines (SVMs) to detect melanoma [29]. In recent research, Codella and his colleagues established a system combining recent developments in deep learning and machine learning approaches for skin lesion segmentation and classification [30]. Kawahara et al. employed a fully convolutional network to extract multi-scale features for melanoma recognition [31]. Yu et al. applied a very deep residual network to distinguish melanoma from non-melanoma lesions [20]. Although lots of work has been proposed, there is still a margin of performance improvement for both skin lesion segmentation and classification. The International Skin Imaging Collaboration (ISIC) is a cooperation focusing on the automatic analysis of skin lesion, and has continuously expanded its datasets since 2016. 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. Different from the extensively studied lesion segmentation and classification, dermoscopic feature extraction is a new task in the area. Consequently, few studies have been proposed to address the problem. In this paper, we proposed deep learning frameworks to address the three main processing tasks of skin lesion images proposed in ISIC 2017. The main contribution of this paper can be summarized as follows:
An approach to detect skin lesion and melanoma based on deep learning model was proposed in REF .
Skin Lesion Analysis towards Melanoma Detection Using Deep Learning Network
Sensors (Basel, Switzerland)
Sensors (Basel, Switzerland)
Computer Science Engineering Medicine