Deep neural networks approach to skin lesions classification — A comparative analysis

By Arkadiusz Kwasigroch, Agnieszka Mikołajczyk, Michal Grochowski. 2017

56 citations recorded by the source corpus.

The paper presents the results of research on the use of Deep Neural Networks (DNN) for automatic classification of the skin lesions. The authors have focused on the most effective kind of DNNs for image processing, namely Convolutional Neural Networks (CNN). In particular, three kinds of CNN were analyzed: VGG19, Residual Networks (ResNet) and the hybrid of VGG19 CNN with the Support Vector Machine (SVM). The research was carried out with the use of database of over 10 000 images representing skin lesions: benign and malignant. Because of an uneven number of images representing different classes of lesions, the up-sampling of underrepresented class was applied. The comparison of the CNN structures with respect to the accuracy, sensitivity and specificity was performed using k-fold validation method.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Oncology

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Citations in the corpus

Style transfer-based image synthesis as an efficient regularization technique in deep learning (2019, methodology): “We aimed to draw more general conclusions and therefore we conducted training and tests on four different architecture types, namely: VGG9, VGG11, VGG16 and DenseNet121 [41], [42].”
A scoping review of transfer learning research on medical image analysis using ImageNet (2020, background): “, [90,94]), and small kernel size over large kernel size (e.”
Deep neural network architecture search using network morphism (2019, background): “The ResNet network performs even worse than VGG16 that we reported in [25].”