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Skin cancer detection through dermoscopy images has remained challenging due to higher inter‐class uniformity and intra‐class diversity. Deep convolutional neural networks (CNNs) have recently obtained remarkable attention for automated skin cancer classification; however, most of these methods extract features from the global image of high resolution. One of the major drawbacks of these methods is the downscaling of input images to a low resolution, which leads to information loss. Moreover, the lack of a vast number of dermoscopy images is a concern. To overcome these issues and improve the classification accuracy, we propose a novel method using patch‐based local deep feature extraction. In the proposed method, the features are extracted from different patches of a dermoscopy image using a pre‐trained CNN model and are fused to preserve fine details. The kernel principal component analysis is then employed to select the prominent features, and a feed‐forward neural network is finally used to detect skin cancer. The approach is validated using two benchmark datasets: ISIC 2016 and ISIC 2017. The experimental results show that the suggested method achieves promising results in comparison with state‐of‐the‐art approaches. Moreover, a comparative analysis is made among contemporary pre‐trained CNN models.

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