Skin cancer is one of the most deadly diseases around the world, wherein one of the three cancers is skin cancer. Early detection of skin cancer is paramount for better treatment planning. This paper investigates a Convolutional Neural Network (CNN), specifically, You Only Look Once (YOLO), to extract features from the skin lesions. The features, obtained from the CNN, are concatenated with traditional features like texture and colour features extracted from the lesion region of the input images. Later, the concatenated features are fed to a Fully Connected Network, which is trained with the specific ground truths to achieve higher classification accuracy. The proposed method improves the detection and classification of skin lesions when compared with other models and YOLO without traditional features. The performance measures of the fusion network are able to achieve the accuracy of 94%, precision of 0.85, recall of 0.88, and area under the curve of 0.95.

皮肤癌是世界上最致命的疾病之一，其中三种癌症之一是皮肤癌。早期发现皮肤癌对于更好的治疗计划至关重要。本文研究了卷积神经网络（CNN），特别是“只看一次”（YOLO），以从皮肤病变中提取特征。从CNN获得的特征与传统特征（例如从输入图像的病变区域提取的纹理和颜色特征）并置。后来，连接的要素被馈送到完全连接的网络，该网络将根据特定的地面情况进行训练，以实现更高的分类精度。与其他模型和没有传统功能的YOLO相比，该方法改善了皮肤病变的检测和分类。