Skin Lesion Classification Based on Surface Fractal Dimensions and Statistical Color Cluster Features Using an Ensemble of Machine Learning Techniques

(1) Background: An approach for skin cancer recognition and classification by implementation of a novel combination of features and two classifiers, as an auxiliary diagnostic method, is proposed. (2) Methods: The predictions are made by k-nearest neighbor with a 5-fold cross validation algorithm an...

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Autores principales:	Simona Moldovanu, Felicia Anisoara Damian Michis, Keka C. Biswas, Anisia Culea-Florescu, Luminita Moraru
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	skin cancer recognition k-nearest neighbor Higuchi fractal dimensions Radial basis function neural network Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282
Acceso en línea:	https://doaj.org/article/7b0ed21fc05d43cfa9992e87f89b22e3
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spelling	oai:doaj.org-article:7b0ed21fc05d43cfa9992e87f89b22e32021-11-11T15:26:30ZSkin Lesion Classification Based on Surface Fractal Dimensions and Statistical Color Cluster Features Using an Ensemble of Machine Learning Techniques10.3390/cancers132152562072-6694https://doaj.org/article/7b0ed21fc05d43cfa9992e87f89b22e32021-10-01T00:00:00Zhttps://www.mdpi.com/2072-6694/13/21/5256https://doaj.org/toc/2072-6694(1) Background: An approach for skin cancer recognition and classification by implementation of a novel combination of features and two classifiers, as an auxiliary diagnostic method, is proposed. (2) Methods: The predictions are made by k-nearest neighbor with a 5-fold cross validation algorithm and a neural network model to assist dermatologists in the diagnosis of cancerous skin lesions. As a main contribution, this work proposes a descriptor that combines skin surface fractal dimension and relevant color area features for skin lesion classification purposes. The surface fractal dimension is computed using a 2D generalization of Higuchi’s method. A clustering method allows for the selection of the relevant color distribution in skin lesion images by determining the average percentage of color areas within the nevi and melanoma lesion areas. In a classification stage, the Higuchi fractal dimensions (HFDs) and the color features are classified, separately, using a kNN-CV algorithm. In addition, these features are prototypes for a Radial basis function neural network (RBFNN) classifier. The efficiency of our algorithms was verified by utilizing images belonging to the 7-Point, Med-Node, and PH2 databases; (3) Results: Experimental results show that the accuracy of the proposed RBFNN model in skin cancer classification is 95.42% for 7-Point, 94.71% for Med-Node, and 94.88% for PH2, which are all significantly better than that of the kNN algorithm. (4) Conclusions: 2D Higuchi’s surface fractal features have not been previously used for skin lesion classification purpose. We used fractal features further correlated to color features to create a RBFNN classifier that provides high accuracies of classification.Simona MoldovanuFelicia Anisoara Damian MichisKeka C. BiswasAnisia Culea-FlorescuLuminita MoraruMDPI AGarticleskin cancer recognitionk-nearest neighborHiguchi fractal dimensionsRadial basis function neural networkNeoplasms. Tumors. Oncology. Including cancer and carcinogensRC254-282ENCancers, Vol 13, Iss 5256, p 5256 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	skin cancer recognition k-nearest neighbor Higuchi fractal dimensions Radial basis function neural network Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282
spellingShingle	skin cancer recognition k-nearest neighbor Higuchi fractal dimensions Radial basis function neural network Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282 Simona Moldovanu Felicia Anisoara Damian Michis Keka C. Biswas Anisia Culea-Florescu Luminita Moraru Skin Lesion Classification Based on Surface Fractal Dimensions and Statistical Color Cluster Features Using an Ensemble of Machine Learning Techniques
description	(1) Background: An approach for skin cancer recognition and classification by implementation of a novel combination of features and two classifiers, as an auxiliary diagnostic method, is proposed. (2) Methods: The predictions are made by k-nearest neighbor with a 5-fold cross validation algorithm and a neural network model to assist dermatologists in the diagnosis of cancerous skin lesions. As a main contribution, this work proposes a descriptor that combines skin surface fractal dimension and relevant color area features for skin lesion classification purposes. The surface fractal dimension is computed using a 2D generalization of Higuchi’s method. A clustering method allows for the selection of the relevant color distribution in skin lesion images by determining the average percentage of color areas within the nevi and melanoma lesion areas. In a classification stage, the Higuchi fractal dimensions (HFDs) and the color features are classified, separately, using a kNN-CV algorithm. In addition, these features are prototypes for a Radial basis function neural network (RBFNN) classifier. The efficiency of our algorithms was verified by utilizing images belonging to the 7-Point, Med-Node, and PH2 databases; (3) Results: Experimental results show that the accuracy of the proposed RBFNN model in skin cancer classification is 95.42% for 7-Point, 94.71% for Med-Node, and 94.88% for PH2, which are all significantly better than that of the kNN algorithm. (4) Conclusions: 2D Higuchi’s surface fractal features have not been previously used for skin lesion classification purpose. We used fractal features further correlated to color features to create a RBFNN classifier that provides high accuracies of classification.
format	article
author	Simona Moldovanu Felicia Anisoara Damian Michis Keka C. Biswas Anisia Culea-Florescu Luminita Moraru
author_facet	Simona Moldovanu Felicia Anisoara Damian Michis Keka C. Biswas Anisia Culea-Florescu Luminita Moraru
author_sort	Simona Moldovanu
title	Skin Lesion Classification Based on Surface Fractal Dimensions and Statistical Color Cluster Features Using an Ensemble of Machine Learning Techniques
title_short	Skin Lesion Classification Based on Surface Fractal Dimensions and Statistical Color Cluster Features Using an Ensemble of Machine Learning Techniques
title_full	Skin Lesion Classification Based on Surface Fractal Dimensions and Statistical Color Cluster Features Using an Ensemble of Machine Learning Techniques
title_fullStr	Skin Lesion Classification Based on Surface Fractal Dimensions and Statistical Color Cluster Features Using an Ensemble of Machine Learning Techniques
title_full_unstemmed	Skin Lesion Classification Based on Surface Fractal Dimensions and Statistical Color Cluster Features Using an Ensemble of Machine Learning Techniques
title_sort	skin lesion classification based on surface fractal dimensions and statistical color cluster features using an ensemble of machine learning techniques
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/7b0ed21fc05d43cfa9992e87f89b22e3
work_keys_str_mv	AT simonamoldovanu skinlesionclassificationbasedonsurfacefractaldimensionsandstatisticalcolorclusterfeaturesusinganensembleofmachinelearningtechniques AT feliciaanisoaradamianmichis skinlesionclassificationbasedonsurfacefractaldimensionsandstatisticalcolorclusterfeaturesusinganensembleofmachinelearningtechniques AT kekacbiswas skinlesionclassificationbasedonsurfacefractaldimensionsandstatisticalcolorclusterfeaturesusinganensembleofmachinelearningtechniques AT anisiaculeaflorescu skinlesionclassificationbasedonsurfacefractaldimensionsandstatisticalcolorclusterfeaturesusinganensembleofmachinelearningtechniques AT luminitamoraru skinlesionclassificationbasedonsurfacefractaldimensionsandstatisticalcolorclusterfeaturesusinganensembleofmachinelearningtechniques
_version_	1718435318691004416

Skin Lesion Classification Based on Surface Fractal Dimensions and Statistical Color Cluster Features Using an Ensemble of Machine Learning Techniques

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