S<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si4.svg" display="inline" id="d1e3717"><mml:msup><mml:mrow /><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msup></mml:math>C-DeLeNet: A parameter transfer based segmentation-classification integration for detecting skin cancer lesions from dermoscopic images-Reference-Cited by-同舟云学术

S2C-DeLeNet: A parameter transfer based segmentation-classification integration for detecting skin cancer lesions from dermoscopic images

Published:2022-11 Issue: Volume:150 Page:106148
ISSN:0010-4825
Container-title:Computers in Biology and Medicine
language:en
Short-container-title:Computers in Biology and Medicine

Author:

Alam Md. Jahin^ORCID,Mohammad Mir Sayeed^ORCID,Hossain Md Adnan Faisal^ORCID,Showmik Ishtiaque Ahmed^ORCID,Raihan Munshi Sanowar^ORCID,Ahmed Shahed,Mahmud Talha Ibn^ORCID

Publisher

Elsevier BV

Subject

Health Informatics,Computer Science Applications

Reference45 articles.

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3. Biomarkers in melanoma and non-melanoma skin cancer prevention and risk stratification;Trager;Exp. Dermatol.,2022

4. FCN-based DenseNet framework for automated detection and classification of skin lesions in dermoscopy images;Adegun;IEEE Access,2020

5. H.K.S. Juboori, Detection of skin melanoma using deep learning approach, Sci. Arch. 2 (4) 330–334.

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