Analysis of clinical and dermoscopic features for basal cell carcinoma neural network classification.
Analysis of clinical and dermoscopic features for basal cell carcinoma neural network classification.
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DOI：
10.1111/j.1600-0846.2012.00630.x
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发表时间：
2013-02
期刊：
影响因子：
--
通讯作者：
Moss RH
中科院分区：
文献类型：
--
作者：
Cheng B;Joe Stanley R;Stoecker WV;Stricklin SM;Hinton KA;Nguyen TK;Rader RK;Rabinovitz HS;Oliviero M;Moss RH
基底细胞癌（BCC）是美国最常见的癌症。在这项研究中，我们研究了用于诊断决策的四种不同特征类别，包括患者个人资料（患者年龄，性别等），一般检查（病变大小和位置），普通皮肤镜检查（蓝灰色卵形，叶结构污垢痕迹等），和特异性皮肤镜病变（白色/粉红色区域、透明性等）。特定皮肤镜特征是普通皮肤镜特征的更受限的版本。在700个病变的数据集上分析了四个特征类别的组合，其中350个基底细胞癌和350个良性病变，使用基于神经网络的技术进行病变鉴别，包括进化人工神经网络和进化人工神经网络集成。基于十倍交叉验证的训练和测试不同的基于神经网络的技术的实验结果产生了接收器工作特征曲线下的面积高达0.981时，所有功能相结合。常见的皮肤镜病变特征通常比其他个体特征类别产生更高的区分结果。实验结果表明，结合临床和图像信息提供了增强的病变区分能力，无论是单独的信息源。这项研究突出了数据融合作为诊断过程模型的潜力。
Basal cell carcinoma (BCC) is the most commonly diagnosed cancer in the United States. In this research, we examine four different feature categories used for diagnostic decisions, including patient personal profile (patient age, gender, etc.), general exam (lesion size and location), common dermoscopic (blue-gray ovoids, leaf-structure dirt trails, etc.), and specific dermoscopic lesion (white/pink areas, semitranslucency, etc.). Specific dermoscopic features are more restricted versions of the common dermoscopic features. Combinations of the four feature categories are analyzed over a data set of 700 lesions, with 350 BCCs and 350 benign lesions, for lesion discrimination using neural network-based techniques, including Evolving Artificial Neural Networks and Evolving Artificial Neural Network Ensembles. Experiment results based on ten-fold cross validation for training and testing the different neural network-based techniques yielded an area under the receiver operating characteristic curve as high as 0.981 when all features were combined. The common dermoscopic lesion features generally yielded higher discrimination results than other individual feature categories. Experimental results show that combining clinical and image information provides enhanced lesion discrimination capability over either information source separately. This research highlights the potential of data fusion as a model for the diagnostic process.
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影响因子：
13.8
作者：
Altamura, Davide;Menzies, Scott W.;Peris, Ketty
通讯作者：
Peris, Ketty
DOI：
10.4018/jhisi.2011100102
发表时间：
2011-10-01
影响因子：
1.1
作者：
Cheng, Beibei;Stanley, R. Joe;Thoma, George R.
通讯作者：
Thoma, George R.
影响因子：
2.2
作者：
Stoecker, William V.;Gupta, Kapil;Kolm, Isabel
通讯作者：
Kolm, Isabel
影响因子：
9
作者：
Reynolds, Robert G.;Ali, Mostafa;Islam, Md. Monirul
通讯作者：
Islam, Md. Monirul
影响因子：
2.2
作者：
Cheng, Beibei;Erdos, David;Gomez, David D.
通讯作者：
Gomez, David D.