A machine learning framework for skin cancer detection using transfer learning

Mary Linda Panakal Augustine; Sotirios Spanogianopoulos; Mhd Saeed Sharif

doi:10.1109/IPAS63548.2025.10924545

A machine learning framework for skin cancer detection using transfer learning

Mary Linda Panakal Augustine^*, Sotirios Spanogianopoulos, Mhd Saeed Sharif

^*Corresponding author for this work

School of Electrical and Mechanical Engineering

University of East London

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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.

Original language	English
Title of host publication	6th IEEE International Conference on Image Processing, Applications and Systems, IPAS 2025 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Number of pages	4
ISBN (Electronic)	9798331506520
ISBN (Print)	9798331506537
DOIs	https://doi.org/10.1109/IPAS63548.2025.10924545
Publication status	Published - 21 Mar 2025
Event	6th IEEE International Conference on Image Processing, Applications and Systems, IPAS 2025 - Lyon, France Duration: 9 Jan 2025 → 11 Jan 2025

Conference

Conference	6th IEEE International Conference on Image Processing, Applications and Systems, IPAS 2025
Country/Territory	France
City	Lyon
Period	9/01/25 → 11/01/25

Keywords

deep learning
image classification
Skin cancer detection
transfer learning

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/IPAS63548.2025.10924545

A machine learning framework for skin cancer detection using transfer learningAccepted author manuscript (Post-print), 276 KBLicence: CC BY

Cite this

@inproceedings{807ec28d91e8442aa64b42299c73e7e9,

title = "A machine learning framework for skin cancer detection using transfer learning",

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.",

keywords = "deep learning, image classification, Skin cancer detection, transfer learning",

author = "Augustine, {Mary Linda Panakal} and Sotirios Spanogianopoulos and Sharif, {Mhd Saeed}",

note = "Publisher Copyright: {\textcopyright} 2025 IEEE.; 6th IEEE International Conference on Image Processing, Applications and Systems, IPAS 2025 ; Conference date: 09-01-2025 Through 11-01-2025",

year = "2025",

month = mar,

day = "21",

doi = "10.1109/IPAS63548.2025.10924545",

language = "English",

isbn = "9798331506537",

booktitle = "6th IEEE International Conference on Image Processing, Applications and Systems, IPAS 2025 - Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

address = "United States",

}

Augustine, MLP, Spanogianopoulos, S & Sharif, MS 2025, A machine learning framework for skin cancer detection using transfer learning. in 6th IEEE International Conference on Image Processing, Applications and Systems, IPAS 2025 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 6th IEEE International Conference on Image Processing, Applications and Systems, IPAS 2025, Lyon, France, 9/01/25. https://doi.org/10.1109/IPAS63548.2025.10924545

A machine learning framework for skin cancer detection using transfer learning. / Augustine, Mary Linda Panakal; Spanogianopoulos, Sotirios; Sharif, Mhd Saeed.
6th IEEE International Conference on Image Processing, Applications and Systems, IPAS 2025 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2025.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - A machine learning framework for skin cancer detection using transfer learning

AU - Augustine, Mary Linda Panakal

AU - Spanogianopoulos, Sotirios

AU - Sharif, Mhd Saeed

PY - 2025/3/21

Y1 - 2025/3/21

N2 - 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.

AB - 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.

KW - deep learning

KW - image classification

KW - Skin cancer detection

KW - transfer learning

UR - http://www.scopus.com/inward/record.url?scp=105002272509&partnerID=8YFLogxK

U2 - 10.1109/IPAS63548.2025.10924545

DO - 10.1109/IPAS63548.2025.10924545

M3 - Conference contribution

AN - SCOPUS:105002272509

SN - 9798331506537

BT - 6th IEEE International Conference on Image Processing, Applications and Systems, IPAS 2025 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 6th IEEE International Conference on Image Processing, Applications and Systems, IPAS 2025

Y2 - 9 January 2025 through 11 January 2025

ER -

A machine learning framework for skin cancer detection using transfer learning

Abstract

Conference

Keywords

UN SDGs

Access to Document

Fingerprint

Cite this