Skin melanoma segmentation using recurrent and convolutional neural networks

Skin melanoma is one of the highly addressed health problems in many countries. Dermatologists diagnose melanoma by visual inspections of mole using clinical assessment tools such as ABCD. However, computer vision tools have been introduced to assist in quantitative analysis of skin lesions. Deep learning is one of the trending machine learning techniques that have been successfully utilized to solve many difficult computer vision tasks. We proposed using a hybrid method that utilizes two popular deep learning methods: convolutional and recurrent neural networks. The proposed method was trained using 900 images and tested on 375 images. Images were obtained from 'Skin Lesion Analysis Toward Melanoma Detection' challenge which was hosted by ISBI 2016 conference. We achieved segmentation average accuracy of 0.98 and Jaccard index of 0.93. Results were compared with other state-of-the-art methods, including winner of ISBI 2016 challenge for skin melanoma segmentation, along with the same evaluation criteria.