Skin cancer is a type of dangerous disease, and early detection is necessary to increases the survival rate. In recent years, deep learning models applied to computerized skin cancer discovery has become a standard. These models can improve their performance by being able to access more data and its main task is to the classification of images. This task is exceptionally valuable in the field of medicine, it has the ability to assist doctors and specialists to make the right decision and diagnose the patient’s condition with high accuracy. In this paper, a deep learning network has been selected and trained by the author for the analysis of more than 24,000 skin cancer images by convolutional neural network (ConvNet) model applying with three architectures (InceptionV3, ResNet, and VGG19) with many parameters to identify the best architectures in the classification of these images and getting extremely acceptable results; and classifying the cancer type as benign or malignant with high accuracy. The dataset contains high-resolution images obtained from the ISIC archive between 2019 and 2020. After all the tests were done, the best architecture is InceptionV3. This architecture has achieved a diagnostic accuracy of approximately 86.90%, precision of 87.47%, sensitivity of 86.14%, and the specificity of 87.66%.

皮肤癌是一种危险的疾病，因此必须及早发现以提高生存率。近年来，应用于计算机化皮肤癌发现的深度学习模型已成为一种标准。这些模型可以访问更多数据，从而提高其性能，其主要任务是对图像进行分类。这项任务在医学领域异常有价值，它能够帮助医生和专家做出正确的决定并高精度地诊断患者的状况。在本文中，作者选择了深度学习网络并对其进行了培训，以通过卷积神经网络（ConvNet）模型应用三种架构（InceptionV3，ResNet，和VGG19）具有许多参数，以识别这些图像的分类中的最佳架构，并获得极其可接受的结果；并以高准确度将癌症类型分类为良性或恶性。该数据集包含从2019年到2020年之间的ISIC档案库中获得的高分辨率图像。完成所有测试后，最好的体系结构是InceptionV3。此体系结构已实现大约86.90％的诊断准确度，87.47％的精确度，86.14％的灵敏度以及87.66％的特异性。