Department of Electronics and Communication Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore, India.
Department of Computer Science, University of South Dakota, Vermillion, USA.
Arch Dermatol Res. 2024 May 25;316(6):275. doi: 10.1007/s00403-024-03076-z.
A skin lesion refers to an area of the skin that exhibits anomalous growth or distinctive visual characteristics compared to the surrounding skin. Benign skin lesions are noncancerous and generally pose no threat. These irregular skin growths can vary in appearance. On the other hand, malignant skin lesions correspond to skin cancer, which happens to be the most prevalent form of cancer in the United States. Skin cancer involves the unusual proliferation of skin cells anywhere on the body. The conventional method for detecting skin cancer is relatively more painful.
This work involves the automated prediction of skin cancer and its types using two stage Convolutional Neural Network (CNN). The first stage of CNN extracts low level features and second stage extracts high level features. Feature selection is done using these two CNN and ABCD (Asymmetry, Border irregularity, Colour variation, and Diameter) technique. The features extracted from the two CNNs are fused with ABCD features and fed into classifiers for the final prediction. The classifiers employed in this work include ensemble learning methods such as gradient boosting and XG boost, as well as machine learning classifiers like decision trees and logistic regression. This methodology is evaluated using the International Skin Imaging Collaboration (ISIC) 2018 and 2019 dataset.
As a result, the first stage CNN which is used for creation of new dataset achieved an accuracy of 97.92%. Second stage CNN which is used for feature selection achieved an accuracy of 98.86%. Classification results are obtained for both with and without fusion of features.
Therefore, two stage prediction model achieved better results with feature fusion.
皮肤损伤是指与周围皮肤相比，表现出异常生长或明显视觉特征的皮肤区域。良性皮肤损伤是非癌性的，通常不会构成威胁。这些不规则的皮肤生长可以有不同的外观。另一方面，恶性皮肤损伤对应于皮肤癌，而皮肤癌恰好是美国最常见的癌症形式。皮肤癌涉及身体任何部位的皮肤细胞异常增殖。检测皮肤癌的传统方法相对较为疼痛。
本工作使用两阶段卷积神经网络（CNN）自动预测皮肤癌及其类型。第一阶段的 CNN 提取低级特征，第二阶段提取高级特征。使用这两个 CNN 和 ABCD（不对称、边界不规则、颜色变化和直径）技术进行特征选择。从两个 CNN 提取的特征与 ABCD 特征融合，并输入分类器进行最终预测。本工作中使用的分类器包括梯度提升和 XGboost 等集成学习方法，以及决策树和逻辑回归等机器学习分类器。该方法使用国际皮肤成像协作（ISIC）2018 年和 2019 年数据集进行评估。
第一阶段 CNN 用于创建新数据集，其准确率达到 97.92%。第二阶段 CNN 用于特征选择，准确率达到 98.86%。对融合和不融合特征的分类结果进行了研究。
因此，两阶段预测模型在融合特征后获得了更好的结果。
