asrdav / lesion-segmentation-melanoma-tl

Automatic Skin Lesion Segmentation and Melanoma Detection: Transfer Learning approach with U-Net and DCNN-SVM

lesion-segmentation-melanoma-tl

Automatic Skin Lesion Segmentation and Melanoma Detection: Transfer Learning approach with U-Net and DCNN-SVM

[email protected]

publication link: https://link.springer.com/chapter/10.1007/978-981-13-7564-4_32

doi: https://doi.org/10.1007/978-981-13-7564-4_32

@cite

Nazi Z.A., Abir T.A. (2020) Automatic Skin Lesion Segmentation and Melanoma Detection: Transfer Learning Approach with U-Net and DCNN-SVM. In: Uddin M., Bansal J. (eds) Proceedings of International Joint Conference on Computational Intelligence. Algorithms for Intelligent Systems. Springer, Singapore

bibtex

@InProceedings{10.1007/978-981-13-7564-4_32,
author="Nazi, Zabir Al
and Abir, Tasnim Azad",
editor="Uddin, Mohammad Shorif
and Bansal, Jagdish Chand",
title="Automatic Skin Lesion Segmentation and Melanoma Detection: Transfer Learning Approach with U-Net and DCNN-SVM",
booktitle="Proceedings of International Joint Conference on Computational Intelligence",
year="2020",
publisher="Springer Singapore",
address="Singapore",
pages="371--381",
abstract="Industrial pollution resulting in ozone layer depletion has influenced increased UV radiation in recent years which is a major environmental risk factor for invasive skin cancer, melanoma, and other keratinocyte cancers. The incidence of deaths from melanoma has risen worldwide in the past two decades. Deep learning has been employed successfully for dermatologic diagnosis. In this work, we present a deep learning-based scheme to automatically segment skin lesions and detect melanoma from dermoscopy images. U-Net was used for segmenting out the lesion from surrounding skin. The limitation of utilizing deep neural networks with limited medical data was solved with data augmentation and transfer learning. In our experiments, U-Net was used with spatial dropout to solve the problem of overfitting, and different augmentation effects were applied to the training images to increase data samples. The model was evaluated on two different datasets. It achieved a mean dice score of 0.87 and a mean Jaccard index of 0.80 on ISIC 2018 dataset. The trained model was assessed on PH2 dataset where it achieved a mean dice score of 0.93 and a mean Jaccard index of 0.87 with transfer learning. For classification of malignant melanoma, a DCNN-SVM model was used where we compared state-of-the-art deep nets as feature extractors to find the applicability of transfer learning in dermatologic diagnosis domain. Our best model achieved a mean accuracy of 92{\%} on PH2 dataset. The findings of this study are expected to be useful in cancer diagnosis research.",
isbn="978-981-13-7564-4"
}