Spitzoid Lesions Diagnosis based on GA feature selection and Random
Forest
- URL: http://arxiv.org/abs/2003.04745v2
- Date: Wed, 3 Jun 2020 13:23:16 GMT
- Title: Spitzoid Lesions Diagnosis based on GA feature selection and Random
Forest
- Authors: Abir Belaala (LINFI Laboratory, Biskra University), Labib Sadek
(Terrissa LINFI Laboratory, Biskra University), Noureddine Zerhouni (FEMTO-ST
Institute, CNRS - UFC / ENSMM / UTBM, Automatic Control and Micro-Mechatronic
Systems), Christine Devalland (Service of Anatomy and Pathology Cytology)
- Abstract summary: 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.
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.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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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