S2C-DeLeNet: A parameter transfer based segmentation-classification integration for detecting skin cancer lesions from dermoscopic images
Skin cancer segmentation and classification using the HAM10000 and ISIC dataset archives.
Best weights Link: https://drive.google.com/drive/folders/171UExC2ALfO_EylqLkS9h-1Stdj2b0Ew?usp=sharing
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HAM_BV14_DV0_light : Segmentation model weight using EffNet-B4 backbone.
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HAM_segclass_BV14_DV0_light_type_3__en_dec_lighter_unfreeze: Final joint classifier weight.
If you find our work useful, kindly consider citation:
@article{ALAM2022106148,
title = {S2C-DeLeNet: A parameter transfer based segmentation-classification integration for detecting skin cancer lesions from dermoscopic images},
journal = {Computers in Biology and Medicine},
pages = {106148},
year = {2022},
issn = {0010-4825},
doi = {https://doi.org/10.1016/j.compbiomed.2022.106148},
url = {https://www.sciencedirect.com/science/article/pii/S0010482522008563},
author = {Md. Jahin Alam and Mir Sayeed Mohammad and Md Adnan Faisal Hossain and Ishtiaque Ahmed Showmik and Munshi Sanowar Raihan and Shahed Ahmed and Talha Ibn Mahmud},
}
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