Bucket of Deep Transfer Learning Features and Classification Models for Melanoma Detection.
Bucket of Deep Transfer Learning Features and Classification Models for Melanoma Detection.
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DOI：
10.3390/jimaging6120129
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发表时间：
2020-11-26
影响因子：
3.2
通讯作者：
Pellino S
中科院分区：
文献类型：
--
作者：
Manzo M;Pellino S
恶性黑色素瘤是皮肤癌中最致命的一种，近年来，其发病率在全球范围内迅速增长。针对性治疗的最有效方法是早期诊断。深度学习算法，特别是卷积神经网络，代表了图像分析和表示的方法。它们优化了功能设计任务，这对于不同类型的图像（包括医疗图像）的自动方法至关重要。在本文中，我们采用预训练的深度卷积神经网络架构进行图像表示，旨在预测皮肤病变黑色素瘤。首先，我们采用迁移学习方法来提取图像特征。其次，我们在集成分类上下文中采用了迁移学习特征。具体来说，该框架在平衡子空间上训练各个分类器，并通过统计措施组合所提供的预测。皮肤病变图像的数据集上进行实验阶段，得到的结果表明所提出的方法相对于国家的最先进的竞争对手的有效性。
Malignant melanoma is the deadliest form of skin cancer and, in recent years, is rapidly growing in terms of the incidence worldwide rate. The most effective approach to targeted treatment is early diagnosis. Deep learning algorithms, specifically convolutional neural networks, represent a methodology for the image analysis and representation. They optimize the features design task, essential for an automatic approach on different types of images, including medical. In this paper, we adopted pretrained deep convolutional neural networks architectures for the image representation with purpose to predict skin lesion melanoma. Firstly, we applied a transfer learning approach to extract image features. Secondly, we adopted the transferred learning features inside an ensemble classification context. Specifically, the framework trains individual classifiers on balanced subspaces and combines the provided predictions through statistical measures. Experimental phase on datasets of skin lesion images is performed and results obtained show the effectiveness of the proposed approach with respect to state-of-the-art competitors.