基于支持向量机分类的黑色素瘤初步检测移动应用

M. Sadiq, Donthi Sankalpa, Karam Ahfid, A. Sagahyroon, S. Dhou
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引用次数: 0

摘要

本文提出了一种移动应用程序,该应用程序使用附着在增强镜头上的手机摄像头来捕捉身体上任何可疑肖像(例如痣)的图像,并能够使用图像处理和机器学习技术来预测它是否是黑色素瘤。对图像进行预处理以去除噪声并分割感兴趣区域(ROI)。将黑色素瘤与正常组织区分开来的特征,如纹理、颜色和几何形状被提取出来。该方法采用支持向量机(SVM)分类算法进行训练和预测。该方法在公开可用的数据集上进行了实现和测试。实验结果表明,该方法能够检测黑色素瘤病例,预测准确率为79%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Preliminary Melanoma Detection Mobile Application using Support Vector Machine Classification
This paper proposes a mobile application that uses a mobile phone camera attached to an enhanced lens to capture images of any suspicious portrusions on the body (e.g. mole) and be able to predict whether it is melanoma using image processing and machine learning techniques. The images are preprocessed to remove the noise and segment the region of interest (ROI). Features that distinguish melanoma from normal tissues are extracted such as the texture, color, and geometrical shape. The proposed method uses Support Vector Machine (SVM) classification algorithm for training and prediction. The proposed method is implemented and tested on publicly available datasets. Experimantal results showed that the method was able to detect the melanoma cases with a prediction accuracy of 79%.
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