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DePicT Melanoma Deep-CLASS: a deep convolutional neural networks approach to classify skin lesion images.

描绘黑色素瘤深分类: 一种深度卷积神经网络方法对皮损图像进行分类。

  • 影响因子:2.51
  • DOI:10.1186/s12859-020-3351-y
  • 作者列表:"Nasiri S","Helsper J","Jung M","Fathi M
  • 发表时间:2020-03-11
Abstract

BACKGROUND:Melanoma results in the vast majority of skin cancer deaths during the last decades, even though this disease accounts for only one percent of all skin cancers' instances. The survival rates of melanoma from early to terminal stages is more than fifty percent. Therefore, having the right information at the right time by early detection with monitoring skin lesions to find potential problems is essential to surviving this type of cancer. RESULTS:An approach to classify skin lesions using deep learning for early detection of melanoma in a case-based reasoning (CBR) system is proposed. This approach has been employed for retrieving new input images from the case base of the proposed system DePicT Melanoma Deep-CLASS to support users with more accurate recommendations relevant to their requested problem (e.g., image of affected area). The efficiency of our system has been verified by utilizing the ISIC Archive dataset in analysis of skin lesion classification as a benign and malignant melanoma. The kernel of DePicT Melanoma Deep-CLASS is built upon a convolutional neural network (CNN) composed of sixteen layers (excluding input and ouput layers), which can be recursively trained and learned. Our approach depicts an improved performance and accuracy in testing on the ISIC Archive dataset. CONCLUSIONS:Our methodology derived from a deep CNN, generates case representations for our case base to use in the retrieval process. Integration of this approach to DePicT Melanoma CLASS, significantly improving the efficiency of its image classification and the quality of the recommendation part of the system. The proposed method has been tested and validated on 1796 dermoscopy images. Analyzed results indicate that it is efficient on malignancy detection.

摘要

背景: 在过去的几十年里,黑色素瘤导致了绝大多数的皮肤癌死亡,尽管这种疾病只占所有皮肤癌病例的 1%。黑色素瘤从早期到晚期的生存率超过 50%。因此,在正确的时间通过监测皮肤病变的早期检测以发现潜在的问题来获得正确的信息对于这种类型的癌症的生存至关重要。 结果: 提出了一种在基于案例推理 (CBR) 系统中使用深度学习对皮肤病变进行早期检测的方法。这种方法已经被用于从所提出的系统的base中检索新的输入图像,描述黑色素瘤深类,以支持用户获得与其请求的问题相关的更准确的建议 (e。g.,疫区的图像)。通过利用ISIC存档数据集分析良恶性黑色素瘤的皮损分类,验证了我们系统的效率。描绘黑色素瘤深度类的内核是建立在由 16 层 (不包括输入和输出层) 组成的卷积神经网络 (CNN) 上的,可以递归地训练和学习。我们的方法描述了在ISIC存档数据集上测试的性能和准确性的改进。 结论: 我们的方法来源于深度CNN,为我们的病例base生成病例表示,用于检索过程。整合这种方法来描述黑色素瘤类别,显著提高了其图像分类的效率和系统推荐部分的质量。该方法已在 1796 幅皮肤镜图像上进行了测试和验证。分析结果表明,它对恶性肿瘤的检测是有效的。

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影响因子:0.96
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皮肤肿瘤方向

皮肤肿瘤是发生在皮肤的细胞增生性疾病,是一种常见病。发生于皮内或皮下组织的新生物,种类很多,临床上分良性肿瘤和恶性肿瘤。恶性肿瘤可以不断增殖,引起转移,威胁生命,称为皮肤癌。

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