论文标题
使用卷积神经网络对皮肤癌图像进行分类
Classification of Skin Cancer Images using Convolutional Neural Networks
论文作者
论文摘要
皮肤癌是最常见的人类恶性肿瘤（美国癌症协会），主要是在视觉上诊断出的，从初始临床筛查开始，然后可能进行皮肤镜（与皮肤）分析，活检和组织病理学检查。当皮肤细胞的DNA发生错误（突变）时，会发生皮肤癌。这些突变导致细胞不受控制并形成大量癌细胞。这项研究的目的是尝试借助卷积神经网络对皮肤病变的图像进行分类。深层神经网络在考虑到环境所表现出的巨大变异性的同时，显示出巨大的图像分类潜力。在这里，我们根据像素值训练了图像，并根据疾病标签对它们进行了分类。该数据集是从开源的Kaggle存储库（Kaggle Dataset）中获取的，该数据集本身是从ISIC（国际皮肤成像协作）存档中获取的。该培训是在多个模型上进行的，并附有转移学习。达到的最高模型精度超过86.65％。使用的数据集可公开使用，以确保上述结果的可信度和可重复性。
Skin cancer is the most common human malignancy(American Cancer Society) which is primarily diagnosed visually, starting with an initial clinical screening and followed potentially by dermoscopic(related to skin) analysis, a biopsy and histopathological examination. Skin cancer occurs when errors (mutations) occur in the DNA of skin cells. The mutations cause the cells to grow out of control and form a mass of cancer cells. The aim of this study was to try to classify images of skin lesions with the help of convolutional neural networks. The deep neural networks show humongous potential for image classification while taking into account the large variability exhibited by the environment. Here we trained images based on the pixel values and classified them on the basis of disease labels. The dataset was acquired from an Open Source Kaggle Repository(Kaggle Dataset)which itself was acquired from ISIC(International Skin Imaging Collaboration) Archive. The training was performed on multiple models accompanied with Transfer Learning. The highest model accuracy achieved was over 86.65%. The dataset used is publicly available to ensure credibility and reproducibility of the aforementioned result.
