Skin cancer is a widespread disease that typically develops on the skin due to frequent exposure to sunlight. Although cancer can appear on any part of the human body, skin cancer accounts for a significant proportion of all new cancer diagnoses worldwide. There are substantial obstacles to the precise diagnosis and classification of skin lesions because of morphological variety and indistinguishable characteristics across skin malignancies. Recently, deep learning models have been used in the field of image-based skin-lesion diagnosis and have demonstrated diagnostic efficiency on par with that of dermatologists. To increase classification efficiency and accuracy for skin lesions, a cutting-edge multi-layer deep convolutional neural network termed SkinLesNet was built in this study. The dataset used in this study was extracted from the PAD-UFES-20 dataset and was augmented. The PAD-UFES-20-Modified dataset includes three common forms of skin lesions: seborrheic keratosis, nevus, and melanoma. To comprehensively assess SkinLesNet’s performance, its evaluation was expanded beyond the PAD-UFES-20-Modified dataset. Two additional datasets, HAM10000 and ISIC2017, were included, and SkinLesNet was compared to the widely used ResNet50 and VGG16 models. This broader evaluation confirmed SkinLesNet’s effectiveness, as it consistently outperformed both benchmarks across all datasets.
皮肤癌是一种因频繁暴露于阳光下而在皮肤上形成的常见疾病。尽管癌症可发生于人体任何部位，但皮肤癌在全球新发癌症诊断中占据显著比例。由于皮肤恶性肿瘤形态多样且特征难以区分，皮肤病变的精确诊断与分类面临重大挑战。近年来，深度学习模型已被应用于基于图像的皮肤病变诊断领域，其诊断效率已与皮肤科医生相当。为提高皮肤病变分类的效率和准确性，本研究构建了一种名为SkinLesNet的前沿多层深度卷积神经网络。本研究使用的数据集提取自PAD-UFES-20数据集并进行了数据增强处理。改进后的PAD-UFES-20数据集包含三种常见皮肤病变类型：脂溢性角化病、痣和黑色素瘤。为全面评估SkinLesNet的性能，本研究将其评估范围扩展至PAD-UFES-20改进数据集之外，额外纳入HAM10000和ISIC2017两个数据集，并将SkinLesNet与广泛使用的ResNet50和VGG16模型进行对比。这项更广泛的评估证实了SkinLesNet的有效性，其在所有数据集上的表现均持续优于两个基准模型。
肿瘤(癌症)患者之家