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 . Then, a feature selection mechanism using genetic algorithm (GA) is applied in Phase II. Finally, in Phase III, a ten-fold cross-validation method is used to compare the performance of seven machine-learning algorithms for classification . Results obtained with SMOTE-Multilayer Perceptron with GA-based 14 features show the highest classification accuracy ( 0.98 ), a sensitivity of 0.99, and a specificity of 0.98, outperforming other Spitzoid lesionsclassification algorithms .
Spitzoid lesions broadly categorized into Spitz Nevus (SN) , Atypical Spitz Tumors (AST) , 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 , in the second stage, in order to eliminate irrelevant features; genetic algorithm is used to select significant features. This later reduces the computational complexity and speed up the data mining process. In the third stage, Random forest classifier is employed to make a decision for two different categories of lesions (Spitz nevus or Atypical Spitz Tumors). The performance of our proposed scheme is evaluated using accuracy, sensitivity, specificity, G-mean, F- measure, ROC and AUC . Results obtained with our SMOTE-GA-RF model with GA-based 16 features show a great performance with accuracy 0.97, F-measure 0.98 , AUC 0.98, and G-mean 0.97.Results obtained in this study have potential to open new opportunities in diagnosis of spitzoid lesions .
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. 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
(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
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. Then, a feature selection mechanism using genetic algorithm (GA) is applied in Phase II. Finally, in Phase III, a ten-fold cross-validation method
, in the second stage, in order to eliminate irrelevant features; genetic algorithm
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compare the performance of seven machine-learning algorithms for classification
select significant features. This later reduces the computational complexity and speed up the data mining process. In the third stage, Random forest classifier is employed to make a decision for two different categories of lesions (Spitz nevus or Atypical Spitz Tumors). The performance of our proposed scheme is evaluated using accuracy, sensitivity, specificity, G-mean, F- measure, ROC and AUC
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SMOTE-Multilayer Perceptron
our SMOTE-GA-RF model
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14 features show the highest classification accuracy (
16 features show a great performance with accuracy 0.97, F-measure
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), a sensitivity of 0.99, and a specificity of 0.98, outperforming other Spitzoid lesionsclassification algorithms
, AUC 0.98, and G-mean 0.97.Results obtained in this study have potential to open new opportunities in diagnosis of spitzoid lesions