一种基于机器学习的黑色素瘤特征解释识别方法

IF 2.5 4区 综合性期刊 Q2 CHEMISTRY, MULTIDISCIPLINARY Applied Sciences-Basel Pub Date : 2023-09-07 DOI:10.3390/app131810076
Zhenwei Li, Qing Ji, Xiaoli Yang, Yu Zhou, Shulong Zhi
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引用次数: 0

摘要

黑色素瘤是一种致命的皮肤癌,早期发现可以有效治疗。迫切需要可靠的计算机辅助诊断(CAD)系统来有效解决这一问题。本文设计了一种基于特征解释的黑色素瘤识别方法。该方法包括预处理、特征提取、特征排序和分类。首先,通过预处理提高图像质量,并使用k-means分割来识别病变区域。然后提取该区域的纹理、颜色和形状特征。这些特征通过特征递归消除(RFE)进一步细化,以优化它们的分类器。分类器包括四核支持向量机(SVM)、逻辑回归(LR)和高斯朴素贝叶斯(GaussianNB)。此外,为了保证模型的泛化,还设计了交叉验证和100个随机实验。实验生成了可解释的特征重要性排名,重要的是,该模型在不同的数据集上表现出稳健的性能。
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An Identification Method of Feature Interpretation for Melanoma Using Machine Learning
Melanoma is a fatal skin cancer that can be treated efficiently with early detection. There is a pressing need for dependable computer-aided diagnosis (CAD) systems to address this concern effectively. In this work, a melanoma identification method with feature interpretation was designed. The method included preprocessing, feature extraction, feature ranking, and classification. Initially, image quality was improved through preprocessing and k-means segmentation was used to identify the lesion area. The texture, color, and shape features of this region were then extracted. These features were further refined through feature recursive elimination (RFE) to optimize them for the classifiers. The classifiers, including support vector machine (SVM) with four kernels, logistic regression (LR), and Gaussian naive Bayes (GaussianNB) were applied. Additionally, cross-validation and 100 randomized experiments were designed to guarantee the generalization of the model. The experiments generated explainable feature importance rankings, and importantly, the model demonstrated robust performance across diverse datasets.
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来源期刊
Applied Sciences-Basel
Applied Sciences-Basel CHEMISTRY, MULTIDISCIPLINARYMATERIALS SCIE-MATERIALS SCIENCE, MULTIDISCIPLINARY
CiteScore
5.30
自引率
11.10%
发文量
10882
期刊介绍: Applied Sciences (ISSN 2076-3417) provides an advanced forum on all aspects of applied natural sciences. It publishes reviews, research papers and communications. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible. There is no restriction on the length of the papers. The full experimental details must be provided so that the results can be reproduced. Electronic files and software regarding the full details of the calculation or experimental procedure, if unable to be published in a normal way, can be deposited as supplementary electronic material.
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