Abstract
Malignant melanoma is the deadliest form of skin cancer and is one of the most rapidly increasing cancers in the world. In this paper, a methodology for the SIIM-ISIC Melanoma Classification Challenge, where the goal is to detect melanoma from dermoscopic images, is described. The EfficientNet family of convolutional neural networks is utilized and extended for identifying malignant melanoma on a dataset of 58,457 dermoscopic images of pigmented skin lesions. This binary classification problem comes with a severe class imbalance, which is tackled using a loss balancing approach. Furthermore, the dataset contains images with different resolution sizes. This property is addressed by considering different model input resolutions. Lastly, an ensembling strategy of models, trained with different activation functions is applied to increase the diversity of the ensembler and to further improve individual results.
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Acknowledgment
This research has been co‐financed by the EU and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH – CREATE – INNOVATE (project code: Transition - T1EDK-01385).
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Tziomaka, M., Maglogiannis, I. (2021). Ensembles of Deep Convolutional Neural Networks for Detecting Melanoma in Dermoscopy Images. In: Nguyen, N.T., Iliadis, L., Maglogiannis, I., Trawiński, B. (eds) Computational Collective Intelligence. ICCCI 2021. Lecture Notes in Computer Science(), vol 12876. Springer, Cham. https://doi.org/10.1007/978-3-030-88081-1_39
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