Automated lesion segmentation and dermoscopic feature segmentation for skin cancer analysis

Segmentation is the first and most important task in computer-based diagnosis of skin cancer since other tasks are relied mainly on accurately segmented lesions. Recently, deep learning as a mainstream method in machine learning has shown promising results on semantic image segmentation. In this paper, we demonstrate applying deep convolutional networks to two main segmentation tasks in melanoma diagnosis, a lesion segmentation task followed by a lesion dermoscopic feature segmentation task. The proposed method is evaluated on a database from ISBI challenge 2016. By using a hybrid model, computation load for the second task decreases and masks provided by lesion segmentation have been used to enhance the results for the feature segmentation task as well. The results are close to the best results of ISBI challenge 2016. The proposed model yields quite promising results although it is based on very initial hybrid model without an aggressive fine-tuning that is heavily required in Deep Learning implementations. Therefore, there is a room for further improvements.