Classifying Melanoma in ISIC Dermoscopic Images Using Efficient Convolutional Neural Networks and De...
Classifying Melanoma in ISIC Dermoscopic Images Using Efficient Convolutional Neural Networks and Deep Transfer Learning
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Edmonton: International Information and Engineering Technology Association (IIETA)
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Language
English
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Publisher
Edmonton: International Information and Engineering Technology Association (IIETA)
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Contents
Melanoma, recognized as the most life-threatening form of skin cancer, poses a significant threat to life expectancy. The timely identification of melanoma plays a crucial role in mitigating the morbidity and mortality associated with skin cancer. Dermoscopic images, acquired through advanced dermoscopic tools, serve as vital resources for the earl...
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Classifying Melanoma in ISIC Dermoscopic Images Using Efficient Convolutional Neural Networks and Deep Transfer Learning
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TN_cdi_proquest_journals_3097397991
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_3097397991
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ISSN
0765-0019
E-ISSN
1958-5608
DOI
10.18280/ts.410211
