article · Journal of X-Ray Science and Technology · 2023
Reduction of overfitting on the highly imbalanced ISIC-2019 skin dataset using deep learning frameworks
Erapaneni Gayatri, S. L. Aarthy
Cited by 12Open access
Abstract
Finally, augmented data with focal loss is given a good classification performance with 98.85% accuracy, 95.52% precision, and 95.93% recall. Matthews Correlation coefficient (MCC) is the best metric to evaluate the quality of multi-class images. It has given outstanding performance by using augmented data and focal loss.
Cite this paper
Gayatri, E., & Aarthy, S. L. (2023). Reduction of overfitting on the highly imbalanced ISIC-2019 skin dataset using deep learning frameworks. Journal of X-Ray Science and Technology, 32(1), 53–68. https://doi.org/10.3233/xst-230204
Gayatri, Erapaneni, and S. L. Aarthy. “Reduction of overfitting on the highly imbalanced ISIC-2019 skin dataset using deep learning frameworks.” Journal of X-Ray Science and Technology, vol. 32, no. 1, 2023, pp. 53–68, https://doi.org/10.3233/xst-230204.
Gayatri, Erapaneni, and S. L. Aarthy. 2023. “Reduction of overfitting on the highly imbalanced ISIC-2019 skin dataset using deep learning frameworks.” Journal of X-Ray Science and Technology 32 (1): 53–68. https://doi.org/10.3233/xst-230204.
Gayatri, E. and Aarthy, S.L. (2023) ‘Reduction of overfitting on the highly imbalanced ISIC-2019 skin dataset using deep learning frameworks’, Journal of X-Ray Science and Technology, 32(1), pp. 53–68. Available at: https://doi.org/10.3233/xst-230204.
@article{gayatri2023reduction,
title = {{Reduction of overfitting on the highly imbalanced ISIC-2019 skin dataset using deep learning frameworks}},
author = {Gayatri, Erapaneni and Aarthy, S. L.},
journal = {Journal of X-Ray Science and Technology},
volume = {32},
number = {1},
pages = {53--68},
year = {2023},
doi = {10.3233/xst-230204}
}Made from the index record. Check names, capitalisation and page numbers against the paper. Need another style? AI for Research has over 10,000.