Computer Aided Early Detection and Classification of Malignant Melanoma

被引：0

作者：

Shafiq, Shoaib ^{[1
]}

Prasad, P. W. C. ^{[1
]}

Alsadoon, Abeer ^{[1
]}

Ali, Salih ^{[2
]}

Elchouemi, Amr ^{[3
]}

机构：

[1] Charles Sturt Univ, Sydney, NSW, Australia

[2] Univ Technol Baghdad, Baghdad, Iraq

[3] Walden Univ, Minneapolis, MN USA

来源：

2018 10TH INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND COMMUNICATION NETWORKS (CICN 2018) | 2018年

关键词：

Skin Cancer; Melanoma Detection; Early Melanoma Detection; Support Vector Machine (SVM); Dermoscopy; Skin Lesions; BORDER;

D O I：

10.1109/CICN.2018.19

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The diagnosis and application of Skin Cancer using Image Processing are a non-invasive technique. Currently, a lot of methods are present in the analysis and diagnosis of lesions, which provide quantitative information regarding a lesion and act as an early-warning method for it. This presented diagnosis can be used in hospitals as an alternate method for skin cancer detection and can help domain experts in reducing the time for its classification. The proposed method focuses on the classification of Skin Cancer with high accuracy by first reducing the noise from the images using Dull-Razor software, then segmenting the image using an automatic segmentation process. Important features are then extracted from the image using the GLCM and basic statistical method. The features are then fed into SVM to classify the image data. The investigation is carried on 50 normal and 50 melanoma images obtained from DermNet and ISIC archive.

引用

页码：92 / 97

页数：6

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