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Deep learning design for benign and malignant classification of skin lesions: a new approach

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Abstract

ResNet50 and VGG-16 models are introduced in this paper with different strategies, with and without preprocessing and with and without Support Vector Machine (SVM). Moreover, both transfer learning and data augmentation are used to solve the problem of lack of tagged data. The fully connected (FC) layer is replaced by the SVM classifier leading to better accuracy. In addition, in our work, we utilize the median filter, contrast enhancement and edge detection, which based on four main steps: noise removal, gradient smoothed image calculations, non-highest suppression and hysteresis thresholding. Also, the k-fold cross validation is performed to authenticate our model’s performance. Three data sets: ISIC 2017 MNIST-HAM10000 and ISBI 2016 are utilized in our proposed work. It is observed that the proposed technique of employing ResNet50 hybridized with SVM achieves the best performance, specifically with the ISIC2017 dataset, producing 99.19% accuracy, 99.32% area under the curve (AUC), 98.98% sensitivity, 98.78% precision, 98.88% F1 score and 2.6988 s computational time.

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Authors and Affiliations

  1. Department of Basic Science, Faculty of Engineering, Pharos University, Alexandria, Egypt

    Wessam M. Salamaa

  2. Department of Electronics and Communications Engineering, College of Engineering and Technology, Arab Academy for Science, Technology and Maritime Transport, Alexandria, Egypt

    Moustafa H. Aly

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  1. Wessam M. Salamaa
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Correspondence to Moustafa H. Aly.

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Salamaa, W.M., Aly, M.H. Deep learning design for benign and malignant classification of skin lesions: a new approach. Multimed Tools Appl 80, 26795–26811 (2021). https://doi.org/10.1007/s11042-021-11000-0

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