The prediction of skin lesions is a challenging task even for experienced dermatologists due to a little contrast between surrounding skin and lesions, the visual resemblance between skin lesions, fuddled lesion border, etc. An automated computer-aided detection system with given images can help clinicians to prognosis malignant skin lesions at the earliest time. Recent progress in deep learning includes dilated convolution known to have improved accuracy with the same amount of computational complexities compared to traditional CNN. To implement dilated convolution, we choose the transfer learning with four popular architectures: VGG16, VGG19, MobileNet, and InceptionV3. The HAM10000 dataset was utilized for training, validating, and testing, which contains a total of 10015 dermoscopic images of seven skin lesion classes with huge class imbalances. The top-1 accuracy achieved on dilated versions of VGG16, VGG19, MobileNet, and InceptionV3 is 87.42%, 85.02%, 88.22%, and 89.81%, respectively. Dilated InceptionV3 exhibited the highest classification accuracy, recall, precision, and f-1 score and dilated MobileNet also has high classification accuracy while having the lightest computational complexities. Dilated InceptionV3 achieved better overall and per-class accuracy than any known methods on skin lesions classification to the best of our knowledge while experimenting with a complex open-source dataset with class imbalances.

中文翻译：

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基于扩张卷积的深度学习皮肤病变分类

由于周围皮肤和病变之间的对比度差，皮肤病变之间的视觉相似性，病变边界模糊不清等，即使对于有经验的皮肤科医生来说，皮肤病变的预测也是一项艰巨的任务。尽早诊断出恶性皮肤病变。深度学习的最新进展包括已知与传统CNN相比在相同的计算复杂度下具有更高准确性的膨胀卷积。为了实现膨胀卷积，我们选择具有四种流行架构的转移学习：VGG16，VGG19，MobileNet和InceptionV3。HAM10000数据集用于训练，验证和测试，其中包含七个皮肤病变类别的10015张皮肤镜图像，类别失衡严重。在膨胀版本的VGG16，VGG19，MobileNet和InceptionV3上实现的top-1准确性分别为87.42％，85.02％，88.22％和89.81％。扩张的InceptionV3展示了最高的分类准确性，召回率，精确度和f-1分数，而扩张的MobileNet在具有最轻的计算复杂性的同时也具有很高的分类准确性。就我们所知，扩张的InceptionV3在皮肤损伤分类方面比任何已知方法都具有更好的总体和每个类的准确性，同时尝试了一个复杂的带有类不平衡的开源数据集。膨胀的InceptionV3展示了最高的分类准确性，召回率，精确度和f-1分数，而膨胀的MobileNet在具有最轻的计算复杂性的同时也具有较高的分类准确性。据我们所知，扩张式InceptionV3在皮肤损伤分类方面比任何已知方法都具有更好的总体和每个类的准确性，同时尝试了一个复杂的带有类不平衡的开源数据集。膨胀的InceptionV3展示了最高的分类准确性，召回率，精确度和f-1分数，而膨胀的MobileNet在具有最轻的计算复杂性的同时也具有较高的分类准确性。据我们所知，扩张式InceptionV3在皮肤损伤分类方面比任何已知方法都具有更好的总体和每个类的准确性，同时尝试了一个复杂的带有类不平衡的开源数据集。