使用 MATLAB 基于 SVM 检测黑色素瘤

Q3 Computer Science 中国图象图形学报 Pub Date : 2023-12-01 DOI:10.18178/joig.11.4.353-358

Radhwan M. W. Khaleel, N. M. Basheer

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引用次数: 0

摘要

皮肤癌已经成为第五种最危险的癌症。黑色素瘤是一种最凶猛的皮肤癌，应该及时发现并治疗，以降低扩散到身体其他器官的风险。本研究旨在通过图像处理提供快速无痛的皮肤癌检测，包括增强和提取有趣的特征，在MATLAB中对感染的皮肤图像进行表征和分类为黑色素瘤或非黑色素瘤。用于插入图像纹理分析的特征是灰度共生矩阵(GLCM)和局部二值模式(LBP)。利用径向基函数核训练支持向量机(SVM)进行黑色素瘤和非黑色素瘤的分类。检测准确率为94.87%。

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Melanoma Detection Based on SVM Using MATLAB

Skin cancer has become the fifth-most dangerous type of cancer. Melanoma, the most ferocious type of skin cancer, should be detected and treated to reduce the risk of spreading to the rest of the body’s organs. This study aims to provide fast and painless detection of skin cancer using image processing, including enhancement and extraction of interesting features for the characterization and classification of infected skin images into melanoma or nonmelanoma in MATLAB. The features used for texture analysis of inserted images are the Gray Level Co-occurrence Matrix (GLCM) and Local Binary Pattern (LBP). The classification of melanoma and non-melanoma is done by training a Support Vector Machine (SVM) using the radial basis function kernel. The accuracy of testing is 94.87%.

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来源期刊

中国图象图形学报 Computer Science-Computer Graphics and Computer-Aided Design

CiteScore

1.20

自引率

0.00%

发文量

6776

期刊介绍： Journal of Image and Graphics (ISSN 1006-8961, CN 11-3758/TB, CODEN ZTTXFZ) is an authoritative academic journal supervised by the Chinese Academy of Sciences and co-sponsored by the Institute of Space and Astronautical Information Innovation of the Chinese Academy of Sciences (ISIAS), the Chinese Society of Image and Graphics (CSIG), and the Beijing Institute of Applied Physics and Computational Mathematics (BIAPM). The journal integrates high-tech theories, technical methods and industrialisation of applied research results in computer image graphics, and mainly publishes innovative and high-level scientific research papers on basic and applied research in image graphics science and its closely related fields. The form of papers includes reviews, technical reports, project progress, academic news, new technology reviews, new product introduction and industrialisation research. The content covers a wide range of fields such as image analysis and recognition, image understanding and computer vision, computer graphics, virtual reality and augmented reality, system simulation, animation, etc., and theme columns are opened according to the research hotspots and cutting-edge topics. Journal of Image and Graphics reaches a wide range of readers, including scientific and technical personnel, enterprise supervisors, and postgraduates and college students of colleges and universities engaged in the fields of national defence, military, aviation, aerospace, communications, electronics, automotive, agriculture, meteorology, environmental protection, remote sensing, mapping, oil field, construction, transportation, finance, telecommunications, education, medical care, film and television, and art. Journal of Image and Graphics is included in many important domestic and international scientific literature database systems, including EBSCO database in the United States, JST database in Japan, Scopus database in the Netherlands, China Science and Technology Thesis Statistics and Analysis (Annual Research Report), China Science Citation Database (CSCD), China Academic Journal Network Publishing Database (CAJD), and China Academic Journal Network Publishing Database (CAJD). China Science Citation Database (CSCD), China Academic Journals Network Publishing Database (CAJD), China Academic Journal Abstracts, Chinese Science Abstracts (Series A), China Electronic Science Abstracts, Chinese Core Journals Abstracts, Chinese Academic Journals on CD-ROM, and China Academic Journals Comprehensive Evaluation Database.