A Machine Learning Framework for Skin Cancer Detection using Transfer Learning

This paper uses deep learning algorithms including InceptionV2, InceptionV3, DenseNet, MobileNet, and VGG19 to improve skin cancer detection. This research aims to improve skin cancer diagnosis. This work aims to determine the most effective evolutionary metrics-based technique to recognizing skin cancer, which is comparable to other diseases. Ultimately, our paper aims to create a realistic skin cancer detection system that uses the best deep learning algorithm. This discovery might improve medical diagnostics, leading to earlier diagnosis and improved healthcare outcomes.

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Publication Details

Published
2025-01-09
DOI
https://doi.org/10.1109/ipas63548.2025.10924545
Citations
1
Primary Topic
Cutaneous Melanoma Detection and Management
Type
article
Field-Weighted Citation Impact
1.05
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article

A Machine Learning Framework for Skin Cancer Detection using Transfer Learning

M. Augustine, Mhd Saeed Sharif, Sotirios Spanogianopoulos
1 citations
Cutaneous Melanoma Detection and Management
1.05
article

A Machine Learning Framework for Skin Cancer Detection using Transfer Learning

M. Augustine, Mhd Saeed Sharif, Sotirios Spanogianopoulos
article en
1 citations

Abstract

This paper uses deep learning algorithms including InceptionV2, InceptionV3, DenseNet, MobileNet, and VGG19 to improve skin cancer detection. This research aims to improve skin cancer diagnosis. This work aims to determine the most effective evolutionary metrics-based technique to recognizing skin cancer, which is comparable to other diseases. Ultimately, our paper aims to create a realistic skin cancer detection system that uses the best deep learning algorithm. This discovery might improve medical diagnostics, leading to earlier diagnosis and improved healthcare outcomes.

University of East London (GB), University of Portsmouth (GB)
Openalex Percentile: Top 24%
Cutaneous Melanoma Detection and Management
1.05
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