Skin Cancer Detection and Classification for Moles Using K-Nearest Neighbor Algorithm

被引：12

作者：

Linsangan, Noel B. ^{[1
]}

Adtoon, Jetron J. ^{[2
]}

机构：

[1] Mapua Univ, Sch Elect Elect & Comp Engn, Manila, Philippines

[2] Univ Mindanao, Coll Engn Educ, Comp Engn Program, Davao, Philippines

来源：

ICBRA 2018: PROCEEDINGS OF 2018 5TH INTERNATIONAL CONFERENCE ON BIOINFORMATICS RESEARCH AND APPLICATIONS | 2018年

关键词：

Moles; Skin Cancer; k-Nearest Neighbors Algorithm; Geometrical Features;

D O I：

10.1145/3309129.3309141

中图分类号：

Q [生物科学];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

The skin protects our body from heat and light of the sun and other threats. One of the illnesses that threaten the skin is the skin cancer. Skin cancer may start with an irregular shaped mole with size greater than a pencil eraser. This study focuses on the non-invasive approach in detecting and classifying skin cancer. Geometrical features of the moles suspected for skin cancer are extracted following the asymmetry, border, and diameter parameters of the ABCD-Rule of Dermoscopy. In particular, greatest and shortest diameter, irregularity index and equivalent diameter are the parameters loaded in the dataset for classification. Classification of mole images is done through k-Nearest Neighbors (k-NN) algorithm. The overall result showed 86.67% accuracy in determining the classification.

引用

页码：47 / 51

页数：5

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