Researchers are proficient in preprocessing skin images but fail in identifying efficient classifiers for classifying skin cancer due to the complex variety of lesion sizes, colors, and shapes. As such, no single classifier is sufficient for classifying skin cancer legions. Convolutional Neural Networks (CNNs) have played an important role in deep learning, as CNNs have proven successful in classification tasks across many fields. However, present day models available for skin cancer classification suffer from not taking important spatial relations between features into consideration. They classify effectively only if certain features are present in the test data, ignoring their relative spatial relation with each other, which results in false negatives. They also lack rotational invariance, meaning that the same legion viewed at different angles may be assigned to different classes, leading to false positives. The Capsule Network (CapsNet) is designed to overcome the above-mentioned problems. Capsule Networks use modules or capsules other than pooling as an alternative to translational invariance. The Capsule Network uses layer-based squashing and dynamic routing. It uses vector-output capsules and max-pooling with routing by agreement, unlike scale-output feature detectors of traditional CNNs. All of which assist in avoiding false positives and false negatives. The Capsule Network architecture is created with many convolution layers and one capsule layer as the final layer. Hence, in the proposed work, skin cancer classification is performed based on CapsNet architecture which can work well with high dimensional hyperspectral images of skin.

中文翻译：

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使用卷积胶囊网络（CapsNet）进行皮肤癌分类

研究人员精通皮肤图像的预处理，但由于病变大小，颜色和形状的复杂性，未能确定用于对皮肤癌进行分类的有效分类器。因此，没有单一的分类器足以对皮肤癌军团进行分类。卷积神经网络（CNN）在深度学习中发挥了重要作用，因为事实证明，CNN在许多领域的分类任务中都是成功的。然而，当今可用于皮肤癌分类的模型遭受了不考虑特征之间的重要空间关系的困扰。仅当测试数据中存在某些特征时，它们才会有效分类，而忽略它们彼此之间的相对空间关系，这会导致假阴性。他们也缺乏旋转不变性，这意味着可以将以不同角度观看的同一军团分配给不同的类别，从而导致误报。胶囊网络（CapsNet）旨在克服上述问题。封装网络使用池以外的模块或封装来替代平移不变性。胶囊网络使用基于层的压缩和动态路由。与传统CNN的比例输出特征检测器不同，它使用矢量输出胶囊和通过协议路由的最大池化。所有这些都有助于避免误报和误报。胶囊网络架构是由许多卷积层和一个胶囊层作为最终层创建的。因此，在拟议的工作中，