Refining skin lesions classification performance using geometric features of superpixels.

Publication date: Jul 15, 2023

This paper introduces superpixels to enhance the detection of skin lesions and to discriminate between melanoma and nevi without false negatives, in dermoscopy images. An improved Simple Linear Iterative Clustering (iSLIC) superpixels algorithm for image segmentation in digital image processing is proposed. The local graph cut method to identify the region of interest (i. e., either the nevi or melanoma lesions) has been adopted. The iSLIC algorithm is then exploited to segment sSPs. iSLIC discards all the SPs belonging to image background based on assigned labels and preserves the segmented skin lesions. A shape and geometric feature extraction task is performed for each segmented SP. The extracted features are fed into six machine learning algorithms such as: random forest, support vector machines, AdaBoost, k-nearest neighbor, decision trees (DT), Gaussian NacEFve Bayes and three neural networks. These include Pattern recognition neural network, Feed forward neural network, and 1D Convolutional Neural Network for classification. The method is evaluated on the 7-Point MED-NODE and PAD-UFES-20 datasets and the results have been compared to the state-of-art findings. Extensive experiments show that the proposed method outperforms the compared existing methods in terms of accuracy.

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Concepts Keywords
Algorithms Algorithm
Forest Classification
Melanoma Compared
Neighbor Geometric
Superpixels Image


Type Source Name
disease MESH melanoma
pathway KEGG Melanoma
disease MESH nevi
drug DRUGBANK Coenzyme M
disease MESH skin cancers
disease MESH carcinoma
disease MESH cancer
drug DRUGBANK Potassium
drug DRUGBANK Aspartame
drug DRUGBANK Isoxaflutole
disease MESH dysplastic nevi
drug DRUGBANK Serine
disease MESH breast cancer
pathway KEGG Breast cancer

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