Before revision: Spitzoid lesions may be largely categorized into Spitz Nevus  , Atypical Spitz Tumors  , and Spitz Melanomas . Classifying a lesion precisely as Atypical Spitz Tumors or AST is challenging and often requires the integration of clinical, histological, and immunohistochemical features to differentiate AST from regular Spitz nevus and malignant Spitz melanomas. Specifically, this paper aims to test several artificial intelligence techniques so as to build a computer aided diagnosis system. A proposed three-phase approach is being implemented. In Phase I, collected data are preprocessed with an effective Synthetic Minority Oversampling TEchnique or SMOTE-based method being implemented to treat the imbalance data problem .
After revision: Spitzoid lesions may be largely categorized into Spitz Nevus , Atypical Spitz Tumors , and Spitz Melanomas (SM). The accurate diagnosis of these lesions is one of the most challenges for dermapathologists; this is due to the high similarities between them. Data mining techniques are successfully applied to situations like these where complexity exists. This study aims to develop an artificial intelligence model to support the diagnosis of Spitzoid lesions. A private spitzoid lesions dataset have been used to evaluate the system proposed in this study. The proposed system has three stages. In the first stage, SMOTE method applied to solve the imbalance data problem .