Preprocessing of Skin Images and Feature Selection for Early Stage of Melanoma Detection using Color Feature Extraction

International Journal of artificial intelligence research

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Title Preprocessing of Skin Images and Feature Selection for Early Stage of Melanoma Detection using Color Feature Extraction
Creator Sari, Yuita Arum
Hapsani, Anggi Gustiningsih
Adinugroho, Sigit
Hakim, Lukman
Mutrofin, Siti
classification algorithm; feature selection; melanoma detection; preprocessing image; skin image preprocessing
Description Preprocessing is an essential part to achieve good segmentation since it affects the feature extraction process. Melanoma have various shapes and their extracted features from image are used for early stage detection. Due to the fact that melanoma is one of dangerous diseases, early detection is required to prevent further phase of cancer from developing. In this paper, we propose a new framework to detect cancer on skin images using color feature extraction and feature selection. The default color space of skin images is RGB, then brightness is added to distinguish the normal and darken area on the skin. After that, average filter and histogram equalization are applied as well for attaining a good color intensities which are capable of determining normal skin from suspicious one. Otsu thresholding is utilized afterwards for melanoma segmentation. There are 147 features extracted from segmented images. Those features are reduced using three types of feature selection algorithms: Linear Discriminant Analysis (LDA), Correlation based Feature Selection (CFS), and Relief. All selected features are classified using k-Nearest Neighbor  (k-NN). Relief is known to be the best feature selection method among others and the optimal k value is 7 with 10-cross validation with accuracy of 0.835 and 0.845, without and with feature selection respectively. The result indicates that the frameworks is applicable for early skin cancer detection.
Publisher STMIK Dharma Wacana
Date 2020-12-05
Type info:eu-repo/semantics/article
Peer-reviewed Article
Format application/pdf
Source International Journal of Artificial Intelligence Research; Vol 4, No 2 (2020): December; 95 - 106
Language eng
Rights Copyright (c) 2021 International Journal of Artificial Intelligence Research

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