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Article

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

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Department of Computer Science and Information Technology, Faculty of Automation, Computers, Electrical Engineering and Electronics, Dunarea de Jos University of Galati, 47 Domneasca Str., 800008 Galati, Romania
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The Modelling & Simulation Laboratory, Dunarea de Jos University of Galati, 47 Domneasca Str., 800008 Galati, Romania
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Department of Chemistry, Physics & Environment, Faculty of Sciences and Environment, Dunarea de Jos University of Galati, 47 Domneasca Str., 800008 Galati, Romania
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Department of Biological Sciences, University of Alabama at Huntsville, Huntsville, AL 35899, USA
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Department of Electronics and Telecommunications, Faculty of Automation, Computers, Electrical Engineering and Electronics, Dunarea de Jos University of Galati, 47 Domneasca Str., 800008 Galati, Romania
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Author to whom correspondence should be addressed.
Academic Editors: Anke Meyer-Baese, Max Zimmermann and Andreas Stadlbauer
Cancers 2021, 13(21), 5256; https://doi.org/10.3390/cancers13215256
Received: 3 October 2021 / Revised: 18 October 2021 / Accepted: 18 October 2021 / Published: 20 October 2021
(This article belongs to the Collection Artificial Intelligence in Oncology)
This study aimed to investigate the efficacy of implementation of novel skin surface fractal dimension features as an auxiliary diagnostic method for melanoma recognition. We therefore examined the skin lesion classification accuracy of the kNN-CV algorithm and of the proposed Radial basis function neural network model. We found an increased accuracy of classification when the fractal analysis is added to the classical color distribution analysis. Our results indicate that by using a reliable classifier, more opportunities exist to detect timely cancerous skin lesions.
(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. View Full-Text
Keywords: skin cancer recognition; k-nearest neighbor; Higuchi fractal dimensions; Radial basis function neural network skin cancer recognition; k-nearest neighbor; Higuchi fractal dimensions; Radial basis function neural network
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MDPI and ACS Style

Moldovanu, S.; Damian Michis, F.A.; Biswas, K.C.; Culea-Florescu, A.; Moraru, L. Skin Lesion Classification Based on Surface Fractal Dimensions and Statistical Color Cluster Features Using an Ensemble of Machine Learning Techniques. Cancers 2021, 13, 5256. https://doi.org/10.3390/cancers13215256

AMA Style

Moldovanu S, Damian Michis FA, Biswas KC, Culea-Florescu A, Moraru L. Skin Lesion Classification Based on Surface Fractal Dimensions and Statistical Color Cluster Features Using an Ensemble of Machine Learning Techniques. Cancers. 2021; 13(21):5256. https://doi.org/10.3390/cancers13215256

Chicago/Turabian Style

Moldovanu, Simona, Felicia A. Damian Michis, Keka C. Biswas, Anisia Culea-Florescu, and Luminita Moraru. 2021. "Skin Lesion Classification Based on Surface Fractal Dimensions and Statistical Color Cluster Features Using an Ensemble of Machine Learning Techniques" Cancers 13, no. 21: 5256. https://doi.org/10.3390/cancers13215256

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