Deep Neural Network for Fuzzy Automatic Melanoma Diagnosis

Melanoma is the most serious type of skin cancer. We consider in this paper diagnosing melanoma based on skin lesion images obtained by common optical cameras. Given the lower quality of such images, we should cope with the imprecision of image data. This paper proposes a CAD system for decision making about the skin lesion severity. We first define the fuzzy modeling of the Bag-of-Words (BoW) of the lesion. Indeed, features are extracted from the skin lesion image related to four criteria inspired by the ABCD rule (Asymmetry, Border, Color, and Differential structures). Based on Fuzzy C-Means (FCM), membership degrees are determined for each BoW. Then, a deep neural network classifier is used for decision making. Based on a public database of 206 lesion images, experimental results demonstrate that the fuzzification of feature modeling presents good results in term of sensitivity (90.1%) and of accuracy (87.5%). A comparative study illustrates that our approach offers the best accuracy and sensitivity.