This paper is dedicated to Computer-Aided Diagnosis CAD for skin cancers in order to help the expert (dermatologist) to diagnose a dermatological lesion as benign or malignant. The need of this kind of tools has largely expressed because of the difficulties that have the expert to distinguish benign lesion from melanoma. One way to help him without a classification is to find and display to the expert the most similar images (lesions) to the query (lesion of the patient). The similarity must be measured using features and their representation inspired from the medical diagnosis rules. In fact, the diagnosis rules known as ABCD mnemonics are very interesting because they describe a lesion using color, texture and shape. In order to approach the system from the reality, we build it as a Content-Based Image Retrieval CBIR scheme. Images are represented as an object model including the features and their representation and a set of belief degrees. The aim is to combine, on one hand, the experts analysis which include their knowledge, experience. but also their subjectivity, inexactness, uncertainty, etc. On the other hand, the ground truth based on biopsy results of all the database lesions. The combination gives to the system the autonomy and let it evolve without needing a relevance feedback.