二分类器减少皮肤病变中黑色素瘤假阴性检测的比较研究

Amith Jooravan, S. Reddy, N. Pillay
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引用次数: 0

摘要

可靠而准确的皮肤病变分类对于皮肤癌,尤其是黑色素瘤的早期诊断至关重要。传统的分类方法需要对病变进行活检。良性和恶性临床特征的重叠可能导致错误的黑色素瘤诊断和/或切除过多的良性病变。本文侧重于使用机器学习来帮助医生进行皮肤病变的非侵入性分类方法,同时优先考虑最大限度地减少假阴性分类。使用的临床特征是基于ABCD规则,代表病变的不对称性,边界,颜色和直径。选择的皮肤镜图像是厚度小于0.76毫米的黑色素瘤病变,对应于癌症的早期阶段。研究的分类方法包括k近邻(KNN)、Naïve贝叶斯和线性支持向量机。(LSVM)。本研究提出使用LSVM机器学习算法将皮肤病变分类为黑色素瘤或非黑色素瘤,在所研究的分类中假阴性率最低。分类准确率为85%,假阴性率为5%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Comparative Study of Binary Classifiers for Reducing False Negative Detection of Melanoma in Skin Lesions
Reliable and accurate classification of a skin lesion is essential to the early diagnosis of skin cancer, especially melanoma. Traditional classification methods require performing a biopsy on the lesion. The overlap of benign and malignant clinical features may lead to incorrect melanoma diagnosis and/or excising an excessive number of benign lesions. This paper focuses on the use of machine learning to aid physicians with the non-invasive classification methodology of skin lesions, whilst prioritising the minimization of false negative classification. The clinical features used are based on the ABCD rule, representing the asymmetry, border, colour and diameter of the lesion. The dermoscopic images chosen are of melanoma lesions less than 0,76mm in thickness which corresponds to the early stages of cancer. The investigated classification methods include K-Nearest neighbours (KNN), Naïve Bayes and linear support vector machine. (LSVM). This research proposes the use of a LSVM machine learning algorithm to classify a skin lesion as being either melanoma or non-melanoma with the lowest false negative rate of the investigated classification. Classification accuracy of 85% and a false negative rate of 5% is achieved.
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