Fuzzy decision ontology for melanoma diagnosis using KNN classifier

Abstract

Melanoma is the most dangerous type of skin cancer when discovered in an advanced stage. Early detection of melanoma improves survival. Several Computer -Aided Diagnosis (CAD) systems are currently developed to speed up early diagnosis. Recently, ontology is widely adapted for describing and diagnosing a disease. For melanoma detection, the ontology reasoning of dermatologists is based on expert rules, such as ABCD rule. Accordingly, dermatologists classify skin lesions in three classes: melanoma, benign, and recommended follow-up class. In this paper, we propose a CAD system based on an ontology for melanoma diagnosis by giving the probability of being melanoma. We first present our ontology focusing on its main concepts involved in ABCD rule: Asymmetry, Border, Color and Differential structures. Accordingly, the Bag-of-Words, modeling these concepts, are generated from extracted features of skin lesion images. An important step in ontology is to define rules relating the different concepts. In our case, these rules allow the fuzzy decision to classify lesion in melanoma, benign or recommended follow-up class with a malignancy probability. Considering the similarity of melanoma cases, the K-Nearest Neighbors approach is applied to make the final decision in case of a recommended follow-up class. Experimental validation on two public datasets of 206 lesion images shows that our approach presents an efficient method of analysis and can be more appropriate for lesion severity classification. It yields a sensitivity of (96%) and an accuracy of (92%), surpassing existing recent approaches on melanoma diagnosis.