论文标题
评估黑色素瘤皮肤病变分类中的基于大数据的CNN模型
Evaluation of Big Data based CNN Models in Classification of Skin Lesions with Melanoma
论文作者
论文摘要
本章提出了一种使用卷积神经网络诊断色素皮肤病变的方法。该体系结构基于循环神经网络，并使用新的CNN模型以及对现有CNN模型的重新训练的修改进行了评估。实验结果表明，与从皮肤镜面图像明确训练的卷积神经网络模型相比，重新培训的CNN模型在重新培训时进行了大型数据集进行了预训练，以识别皮肤Le-sion类型，以识别皮肤Le-sion类型，以识别皮肤Le-sion类型，以确定皮肤Le-sion类型，从而提供了更准确的结果。通过重新训练Resnet-50卷积神经网络的修改版本，精度等于93.89％，可以实现最佳性能。对皮肤病变病变类型的分析还具有分类精度，分别针对黑色素瘤和基础细胞癌的分类精度分别等于79.13％和82.88％。
This chapter presents a methodology for diagnosis of pigmented skin lesions using convolutional neural networks. The architecture is based on convolu-tional neural networks and it is evaluated using new CNN models as well as re-trained modification of pre-existing CNN models were used. The experi-mental results showed that CNN models pre-trained on big datasets for gen-eral purpose image classification when re-trained in order to identify skin le-sion types offer more accurate results when compared to convolutional neural network models trained explicitly from the dermatoscopic images. The best performance was achieved by re-training a modified version of ResNet-50 convolutional neural network with accuracy equal to 93.89%. Analysis on skin lesion pathology type was also performed with classification accuracy for melanoma and basal cell carcinoma being equal to 79.13% and 82.88%, respectively.
