Skin cancer is a complex public health problem and one of the most common types of cancer worldwide. A biopsy of the skin lesion gives the definitive diagnosis of skin cancer. However, before the definitive diagnosis, specialists observe some symptoms that justify the request for a biopsy and consider a early diagnosis. Early diagnosis of skin cancer is subject to errors due to the lack of experience of specialists and similar characteristics with other diseases. This work proposes a CNN architecture, called EfficientAttentionNet, to provide early diagnosis of melanoma and non-melanoma skin lesions. The methodology represents the stages of development of the proposed classification model and the benefits of each stage. In the first step, the set of images from the International Society for Digital Skin Imaging (ISDIS) is pre-processed to eliminate the hair around the skin lesion. Then, a Generative Adversarial Networks (GAN) model generates synthetic images to balance the number of samples per class in the training set. In addition, a U-net model creates masks for regions of interest in the images. Finally, EfficientAttentionNet training with the mask-based attention mechanism to classify skin lesions. The proposed model achieved high performance, being a reference for future research in the classification of skin lesions.

皮肤癌是一个复杂的公共卫生问题，也是全球最常见的癌症类型之一。皮肤病变活检可明确诊断为皮肤癌。然而，在明确诊断之前，专家会观察一些症状，证明活检的要求是合理的，并考虑早期诊断。由于缺乏专家经验以及与其他疾病相似的特征，皮肤癌的早期诊断容易出错。这项工作提出了一种称为 EfficientAttentionNet 的 CNN 架构，以提供黑色素瘤和非黑色素瘤皮肤病变的早期诊断。该方法代表了所提出的分类模型的发展阶段以及每个阶段的好处。在第一步中，来自国际数字皮肤成像协会 (ISDIS) 的一组图像经过预处理以消除皮肤病变周围的毛发。然后，生成对抗网络 (GAN) 模型生成合成图像以平衡训练集中每个类别的样本数量。此外，U-net 模型为图像中的感兴趣区域创建掩码。最后，EfficientAttentionNet 训练使用基于 mask 的注意力机制对皮肤病变进行分类。该模型取得了较高的性能，为未来皮肤病变分类研究提供了参考。最后，EfficientAttentionNet 训练使用基于 mask 的注意力机制对皮肤病变进行分类。该模型取得了较高的性能，为未来皮肤病变分类研究提供了参考。最后，EfficientAttentionNet 训练使用基于 mask 的注意力机制对皮肤病变进行分类。该模型取得了较高的性能，为未来皮肤病变分类研究提供了参考。