Skin lesion diagnosis with imbalanced ECOC ensembles

Ali Ahmed, Sara Atito and Yanıkoğlu, Berrin and Zor, Cemre and Awais, Muhammad and Kittler, Josef (2021) Skin lesion diagnosis with imbalanced ECOC ensembles. In: 6th International Conference on Machine Learning, Optimization, and Data Science, LOD 2020, Siena, Italy

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Diagnosis of skin lesions is a challenging task due to the similarities between different lesion types, in terms of appearance, location, and size. We present a deep learning method for skin lesion classification by fine-tuning three pre-trained deep learning architectures (Xception, Inception-ResNet-V2, and NasNetLarge), using the training set provided by ISIC2019 organizers. We combine deep convolutional networks with the Error Correcting Output Codes (ECOC) framework to address the open set classification problem and to deal with the heavily imbalanced dataset of ISIC2019. Experimental results show that the proposed framework achieves promising performance that is comparable with the top results obtained in the ISIC2019 challenge leaderboard.
Item Type: Papers in Conference Proceedings
Uncontrolled Keywords: Classification; Data augmentation; Deep learning; ECOC; Ensemble; Imbalanced dataset; Skin cancer
Divisions: Faculty of Engineering and Natural Sciences
Depositing User: Berrin Yanıkoğlu
Date Deposited: 09 Aug 2023 14:49
Last Modified: 09 Aug 2023 14:49

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