Journal of Bioengineering & Biomedical Science

ISSN: 2155-9538

Open Access

Interpretable Aide Diagnosis System for Melanoma Recognition


Messadi M*, Ammar M, Cherifi H, Chikh MA, Bessaid A

During the last years, computer vision-based diagnosis systems have been widely used in several hospitals and dermatology clinics, aiming mostly at the early detection of malignant melanoma tumor, which is among the most frequent types of skin cancer, versus other types of non-malignant cutaneous diseases. The mortality rate can be decreased by earlier detection of suspicious lesions and better prevention. The aim of this paper is to propose an interpretable classification method for skin tumors in dermoscopic images based on shape descriptors. This work presents a fuzzy rule based classifier to discriminate a melanoma. An adaptive Neuro Fuzzy inference System (ANFIS) is applied in order to discover the fuzzy rules leading to the correct classification. In the first step of the proposed work, we apply the Dullrazor technique to reduce the influence of small structures, hairs, bubbles, light reflexion. In the second step, an unsupervised approach for lesion segmentation is proposed. Iterative thresholding is applied to initialize level set automatically. In this paper, we have also treated the necessity to extract all the specific attributes used to develop a characterization methodology that enables specialists to take the best possible diagnosis. For this purpose, our proposal relies largely on visual observation of the tumor while dealing with some characteristics such as color, texture or form. The method used in this paper is called ABCD. It requires calculating 4 factors: Asymmetry (A), Border (B), Color (C) and Diversity (D). These parameters are used to construct a classification module based on ANFIS for the recognition of malignant melanoma. Finally, we compare the results of classification obtained by ANFIS with SVM (support vector machine) and artificial neural network, and discuss how these results may influence in the following steps: the feature extraction and the final lesion classification. This framework has been tested on a dermoscopic database of 320 images. Experimental results show that the proposed method is effective in improving the interpretability of the fuzzy classifier while preserving the model performances at a satisfactory level.


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