A Classification of Skin Lesion using Fractional Poisson for Texture Feature Extraction

被引：1
|
作者：
Al-abayechi, Alaa Ahmed Abbas [1 ]
Jalab, Hamid A. [2 ]
Ibrahim, Rabha W. [2 ]
机构：
[1] Middle Tech Univ, Al Rusafa Management Inst, Baghdad, Iraq
[2] Univ Malaya, Fac Comp Sci & Informat Technol, Kuala Lumpur, Malaysia
来源：
PROCEEDINGS OF THE SECOND INTERNATIONAL CONFERENCE ON INTERNET OF THINGS, DATA AND CLOUD COMPUTING (ICC 2017) | 2017年
关键词：
Skin lesion; texture; feature extraction; fractional Poisson process; image segmentation; classification; DIAGNOSIS;
D O I：
10.1145/3018896.3036379
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Melanoma has been widely recognized as a dangerous type of skin cancer due to its ability to rapidly spread through the human body. An early detection of melanoma has proven to improve the success rate of clinical treatment. This problem may further extend to the field of computerized melanoma detections. In this study, we propose a method using texture features based on fractional Poisson to classify melanoma. Eight masks were created for eight direction. Mean and standard deviation were computed for each direction to create feature vector from images. Three classification models: Support Vector Machine (SVM), AdaBoost-M1, and K-nearest neighbors algorithm (K-NN) were used for separating melanoma from other types of skin lesions. It was found that AdaBoost-M1 provided the highest 100% results for the correct classification rate, specificity and sensitivity.
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收藏
页数：7
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