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Apply Lightweight Deep Learning on Internet of Things for Low-Cost and Easy-To-Access Skin Cancer Detection

机译：在物联网上应用轻量级深度学习，以进行低成本且易于访问的皮肤癌检测

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Melanoma is the most dangerous form of skin cancer that often resembles molos. Dermatologists often recommend regular skin examination to identify and eliminate Melanoma in its early stages. To facilitate this process, we propose a hand-held computer (smart-phone. Raspberry Pi) based assistant that classifies with the dermatologist-level accuracy skin lesion images into malignant, and benign and works in a standalone mobile device without requiring network connectivity. In this paper, we propose and implement a hybrid approach based on advanced deep learning model and domain-specific knowledge and features that dermatologists use for the inspection purpose to improve the accuracy of classification between benign and malignant skin lesions. Here, domain-specific features include the texture of the lesion boundary, the symmetry of the mole, and the boundary characteristics of the region of interest. We also obtain standard deep features from a pre-trained network optimized for mobile devices called Google's Mobile Net. The experiments conducted on ISIC 2017 skin cancer classification challenge demonstrate the effectiveness and complementary nature of these hybrid features over the standard deep features. We performed experiments with the training, testing and validation data splits provided in the competition. Our method achieved area of 0.805 under the receiver operating characteristic curve. Our ultimate goal is to extend the trained model in a commercial hand-held mobile and sensor device such as Raspberry Pi and democratize the access to preventive health care.
机译：黑色素瘤是皮肤癌中最危险的形式，通常类似于痣。皮肤科医生经常建议定期进行皮肤检查，以在早期发现并消除黑色素瘤。为促进此过程，我们建议使用基于手持计算机（智能手机，Raspberry Pi）的助手，以皮肤科医生级别的准确性将皮肤病变图像分类为恶性，良性，并且可以在独立的移动设备中工作，而无需网络连接。在本文中，我们提出并实施了一种基于高级深度学习模型和特定领域知识以及皮肤科医生用于检查目的的特征的混合方法，以提高良性和恶性皮肤病变之间的分类准确性。在此，特定领域的特征包括病变边界的纹理，痣的对称性以及感兴趣区域的边界特征。我们还从针对Google的Mobile Net的移动设备进行了优化的预训练网络中获得了标准的深度功能。在ISIC 2017皮肤癌分类挑战中进行的实验证明了这些混合特征在标准深度特征上的有效性和互补性。我们使用比赛中提供的训练，测试和验证数据进行了实验。我们的方法在接收器工作特性曲线下获得了0.805的面积。我们的最终目标是在Raspberry Pi等商用手持式移动和传感器设备中扩展训练有素的模型，并使预防性保健的民主化。

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