Automatic Detection of Melanoma Skin Cancer using Texture Analysis
Automatic Detection of Melanoma Skin Cancer using Texture Analysis
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DOI：
10.5120/5817-8129
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发表时间：
2012-03
期刊：
影响因子：
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通讯作者：
M. Mabrouk;Mariam A. Sheha;A. Sharawy
中科院分区：
文献类型：
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作者：
M. Mabrouk;Mariam A. Sheha;A. Sharawy
黑色素瘤被认为是最危险的一种皮肤癌。早期和准确诊断主要取决于重要问题、特征提取的准确性和分类器方法的效率。本文提出了一种应用于一组皮肤镜图像的黑色素瘤自动诊断方法。提取的特征基于灰度共生矩阵（GLCM），并使用多层感知器分类器（MLP）对黑素细胞痣和恶性黑色素瘤进行分类。在训练和测试过程中提出了两种不同的MLP分类器：自动MLP和传统MLP。结果表明，纹理分析是一种判别黑色素细胞性皮肤肿瘤的有效方法，具有较高的准确率。第一种技术是自动迭代计数器，速度更快，而第二种技术是默认迭代计数器，准确率更高，训练集为100%，测试集为92%。
Melanoma is considered the most dangerous type of skin cancer. Early and accurate diagnosis depends mainly on important issues, accuracy of feature extracted and efficiency of classifier method. This paper presents an automated method for melanoma diagnosis applied on a set of dermoscopy images. Features extracted are based on gray level Co-occurrence matrix (GLCM) and Using Multilayer perceptron classifier (MLP) to classify between Melanocytic Nevi and Malignant melanoma. MLP classifier was proposed with two different techniques in training and testing process: Automatic MLP and Traditional MLP. Results indicated that texture analysis is a useful method for discrimination of melanocytic skin tumors with high accuracy. The first technique, Automatic iteration counter is faster but the second one, Default iteration counter gives a better accuracy, which is 100 % for the training set and 92 % for the test set.