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. 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. 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.

中文翻译：

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DePicT Melanoma Deep-CLASS：一种对皮肤病变图像进行分类的深度卷积神经网络方法。

在过去的几十年里，黑色素瘤导致了绝大多数皮肤癌死亡，尽管这种疾病只占所有皮肤癌病例的百分之一。黑色素瘤从早期到晚期的存活率超过百分之五十。因此，通过早期检测和监测皮肤病变来发现潜在问题，在正确的时间获得正确的信息对于生存这种类型的癌症至关重要。提出了一种使用深度学习对皮肤病变进行分类的方法，以便在基于案例的推理（CBR）系统中早期检测黑色素瘤。这种方法已用于从所提出的系统 DePicT Melanoma Deep-CLASS 的案例库中检索新的输入图像，以支持用户提供与其请求的问题相关的更准确的建议（例如，受影响区域的图像）。我们的系统的效率已经通过利用 ISIC 档案数据集分析皮肤病变分类为良性和恶性黑色素瘤而得到验证。DePicT Melanoma Deep-CLASS的内核建立在由16层（不包括输入层和输出层）组成的卷积神经网络（CNN）之上，可以递归地训练和学习。我们的方法描述了 ISIC 档案数据集测试的性能和准确性的提高。我们的方法源自深度 CNN，为我们的案例库生成案例表示，以便在检索过程中使用。将此方法集成到 DePicT Melanoma CLASS 中，显着提高了其图像分类的效率和系统推荐部分的质量。所提出的方法已在 1796 幅皮肤镜图像上进行了测试和验证。分析结果表明它对于恶性肿瘤检测是有效的。