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Abstract
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.
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Details
- Category:
- Conference activity
- Type:
- publikacja w wydawnictwie zbiorowym recenzowanym (także w materiałach konferencyjnych)
- Title of issue:
- 2017 22nd International Conference on Methods and Models in Automation and Robotics (MMAR) strony 1043 - 1048
- Language:
- English
- Publication year:
- 2017
- Bibliographic description:
- Kwasigroch A., Mikołajczyk A., Grochowski M.: Deep neural networks approach to skin lesions classification — A comparative analysis// 2017 22nd International Conference on Methods and Models in Automation and Robotics (MMAR)/ : Institute of Electrical and Electronics Engineers (IEEE), 2017, s.1043-1048
- DOI:
- Digital Object Identifier (open in new tab) 10.1109/mmar.2017.8046978
- Verified by:
- Gdańsk University of Technology
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