research-article

Skin Cancer Detection using Ensemble Learning and Grouping of Deep Models

Authors:

Takfarines Guergueb,

Moulay A. AkhloufiAuthors Info & Claims

CBMI '22: Proceedings of the 19th International Conference on Content-based Multimedia Indexing

Pages 121 - 125

https://doi.org/10.1145/3549555.3549584

Published: 07 October 2022 Publication History

Abstract

Melanoma remains the most dangerous form of skin cancer which has a high mortality rate. When detect early, melanoma can be easily cured and millions of lives might be saved. The use of automatic detection models in clinical decision support can increase the ability to address this issue and improve survival rates. In this work, we proposed an automated pipeline for melanoma detection, which combines the predictions of deep convolutional neural network models through ensemble learning techniques. Furthermore, our automated pipeline includes various strategies such as image augmentation, upsampling, image cropping, digital hair removal and class weighting. Our pipeline was trained and tested using the image data acquired from the Society for Imaging Informatics in Medicine and the International Skin Imaging Collaboration SIIM-ISIC 2020. Our proposed pipeline has demonstrated a high performance compared to the other state-of-the-art pipelines for melanoma disease prediction with an accuracy of 97.77% and an AUC of 98.47%.

References

[1]

Faruk Alendar, Irdina Drljević, Kenan Drljević, and Temeida Alendar. 2009. Early Detection of Melanoma Skin Cancer. 9 (Feb. 2009), 77–80. https://doi.org/10.17305/bjbms.2009.2861

[2]

J. Brownlee. 2019. What is Deep Learning?Retrieved accessed Dec. 02, 2021) from https://machinelearningmastery.com/what-is-deep-learning/

[3]

Alliance Seattle Cancer Care. 2021. cancer early detection | Seattle Cancer Care Alliance. Retrieved accessed Dec. 02, 2021. from https://www.seattlecca.org/prevention/skin-cancer-early-detection

[4]

Terrance DeVries and Graham W. Taylor. 2017. Improved Regularization of Convolutional Neural Networks with Cutout.

[5]

Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby. 2020. An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

[6]

Takfarines Guergueb and Moulay A. Akhloufi. 2021. Melanoma Skin Cancer Detection Using Recent Deep Learning Models. In 2021 43rd Annual International Conference of the IEEE Engineering in Medicine Biology Society (EMBC). 3074–3077. https://doi.org/10.1109/EMBC46164.2021.9631047

[7]

Christof Henkel. 2021. Efficient large-scale image retrieval with deep feature orthogonality and Hybrid-Swin-Transformers. arxiv:2110.03786 [cs.CV]

[8]

Md. Arman Hossin, Farhan Fuad Rupom, Hasibur Rashid Mahi, Anik Sarker, Farshid Ahsan, and Sadman Warech. 2020. Melanoma Skin Cancer Detection Using Deep Learning and Advanced Regularizer. In 2020 International Conference on Advanced Computer Science and Information Systems (ICACSIS). 89–94. https://doi.org/10.1109/ICACSIS51025.2020.9263118

[9]

Jie Hu, Li Shen, and Gang Sun. 2018. Squeeze-and-Excitation Networks. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. 7132–7141. https://doi.org/10.1109/CVPR.2018.00745

[10]

ISICs. 2021. International Skin Imaging Collaboration ISICs Archive. Retrieved Online; accessed February 2021 from https://challenge.isic-archive.com/landing

[11]

Joost Koehoorn, André Sobiecki, Paulo Rauber, Andrei Jalba, and Alexandru Telea. 2016. Effcient and Effective Automated Digital Hair Removal from Dermoscopy Images. Mathematical Morphology - Theory and Applications 1 (2016), 1–17.

[12]

Geert Litjens, Thijs Kooi, Babak Ehteshami Bejnordi, Arnaud Arindra Adiyoso Setio, Francesco Ciompi, Mohsen Ghafoorian, Jeroen A.W.M. van der Laak, Bram van Ginneken, and Clara I. Sánchez. 2017. A survey on deep learning in medical image analysis. Medical Image Analysis 42 (2017), 60–88. https://doi.org/10.1016/j.media.2017.07.005

[13]

Ze Liu, Han Hu, Yutong Lin, Zhuliang Yao, Zhenda Xie, Yixuan Wei, Jia Ning, Yue Cao, Zheng Zhang, Li Dong, Furu Wei, and Baining Guo. 2022. Swin Transformer V2: Scaling Up Capacity and Resolution. In International Conference on Computer Vision and Pattern Recognition (CVPR).

[14]

Quoc V. Le Mingxing Tan. 2021. EfficientNetV2: Smaller Models and Faster Training. In Proceedings of the 38th International Conference on Machine Learning. IEEE, 10096–10106.

[15]

Dr.S.Shanthi Ms.M.Pyingkodi, Dr.T.M.Saravanan K Thenmozhi, and Y.Sudarshan D.Hemalatha. 2020. Skin Cancer Classification Towards Melanoma Detection With Deep Learning Techniques. International Journal of Advanced Science and Technology 29, 9s (May 2020), 3911 – 3918. http://sersc.org/journals/index.php/IJAST/article/view/16644

[16]

National Health Service (NHS). 2017. Melanoma skin cancer. Retrieved accessed Dec. 02, 2021. from https://www.nhs.uk/conditions/melanoma-skin-cancer/

[17]

Gehad Ismail Sayed, Mona M. Soliman, and Aboul Ella Hassanien. 2021. A novel melanoma prediction model for imbalanced data using optimized SqueezeNet by bald eagle search optimization. Computers in Biology and Medicine 136 (2021), 104712. https://doi.org/10.1016/j.compbiomed.2021.104712

Digital Library

[18]

Mohammad Shorfuzzaman. 2021. An explainable stacked ensemble of deep learning models for improved melanoma skin cancer detection. Multimedia Systems (2021), 1–15.

[19]

Leslie N. Smith. 2018. A disciplined approach to neural network hyper-parameters: Part 1 - learning rate, batch size, momentum, and weight decay. ArXiv abs/1803.09820(2018).

[20]

American Cancer Society. 2022. What Is Melanoma Skin Cancer? | What Is Melanoma?Retrieved accessed Dec. 02, 2021. from https://www.cancer.org/cancer/melanoma-skin-cancer/about/what-is-melanoma.html

[21]

Mingxing Tan and Quoc Le. 2019. EfficientNet: Rethinking model scaling for convolutional neural networks. In International Conference on Machine Learning. PMLR, 6105–6114.

[22]

Alexandru Telea. 2004. An Image Inpainting Technique Based on the Fast Marching Method. Journal of Graphics Tools 9, 1 (2004), 23–34. https://doi.org/10.1080/10867651.2004.10487596

[23]

Priya Goyal Tsung-Yi Lin, Kaiming He Ross Girshick, and Junsuk Piotr Dollár. 2017. Focal Loss for Dense Object Detection. In 2017 IEEE International Conference on Computer Vision (ICCV). IEEE Computer Society, Los Alamitos, CA, USA, 2999–3007. https://doi.org/10.1109/ICCV.2017.324

[24]

Cancer Research UK. 2020. Melanoma skin cancer | Cancer Research UK. Retrieved accessed Dec. 02, 2021. from https://www.cancerresearchuk.org/about-cancer/melanoma

Cited By

Natha PTera SChinthaginjala RRab SNarasimhulu CKim T(2025)Boosting skin cancer diagnosis accuracy with ensemble approachScientific Reports10.1038/s41598-024-84864-515:1Online publication date: 8-Jan-2025
https://doi.org/10.1038/s41598-024-84864-5
Mohanty BSinghal AKumar BYadav PKanungo PBarik R(2025)Advanced Diagnostic Framework with Vision Transformer for Multiclass Skin Disease ClassificationComputational Intelligence in Pattern Recognition10.1007/978-981-97-8090-7_34(467-480)Online publication date: 5-Mar-2025
https://doi.org/10.1007/978-981-97-8090-7_34
Natha PRajaRajeswari P(2024)Advancing Skin Cancer Prediction Using Ensemble ModelsComputers10.3390/computers1307015713:7(157)Online publication date: 21-Jun-2024
https://doi.org/10.3390/computers13070157
Show More Cited By

Skin Cancer Detection using Ensemble Learning and Grouping of Deep Models
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches

Recommendations

Image Classification of Skin Cancer: Using Deep Learning as a Tool for Skin Self-examinations
Mathematical and Computational Oncology
Abstract
Skin cancer is the most common cancer in the United States, and studies indicate that its incidence is rapidly increasing. Regular skin self-examinations enable early cancer detection and intervention and are recommended in addition to clinician-...
Skin Cancer Classification Using Different Backbones of Convolutional Neural Networks
Advances and Trends in Artificial Intelligence. Theory and Practices in Artificial Intelligence
Abstract
Melanoma is the deadliest of many different types of skin cancer. Clinical screening is followed by dermoscopic analysis and histopathological examination in the diagnosis of melanoma. Melanoma is a type of skin cancer that is highly curable if ...
An explainable stacked ensemble of deep learning models for improved melanoma skin cancer detection
Abstract
Malignant melanoma is one of the most dreadful skin cancer types caused by the abnormal growth of melanocyte cells. Deep convolutional neural network (CNN) models are becoming prominent for the automated diagnosis of melanoma from dermoscopic ...

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences

CBMI '22: Proceedings of the 19th International Conference on Content-based Multimedia Indexing

September 2022

208 pages

ISBN:9781450397209

DOI:10.1145/3549555

Copyright © 2022 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 October 2022

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Funding Sources

WestGrid
Compute Canada
New Brunswick Health Research Foundation
New Brunswick Innovation Foundation

Conference

CBMI 2022

CBMI 2022: International Conference on Content-based Multimedia Indexing

September 14 - 16, 2022

Graz, Austria

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

14
Total Citations
View Citations
101
Total Downloads

Downloads (Last 12 months)22
Downloads (Last 6 weeks)3

Reflects downloads up to 03 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Natha PTera SChinthaginjala RRab SNarasimhulu CKim T(2025)Boosting skin cancer diagnosis accuracy with ensemble approachScientific Reports10.1038/s41598-024-84864-515:1Online publication date: 8-Jan-2025
https://doi.org/10.1038/s41598-024-84864-5
Mohanty BSinghal AKumar BYadav PKanungo PBarik R(2025)Advanced Diagnostic Framework with Vision Transformer for Multiclass Skin Disease ClassificationComputational Intelligence in Pattern Recognition10.1007/978-981-97-8090-7_34(467-480)Online publication date: 5-Mar-2025
https://doi.org/10.1007/978-981-97-8090-7_34
Natha PRajaRajeswari P(2024)Advancing Skin Cancer Prediction Using Ensemble ModelsComputers10.3390/computers1307015713:7(157)Online publication date: 21-Jun-2024
https://doi.org/10.3390/computers13070157
Nasra P(2024)Enhanced Melanoma Detection Using a Fine-Tuned EfficientNetV2-L Model on Dermoscopic Images2024 Second International Conference on Intelligent Cyber Physical Systems and Internet of Things (ICoICI)10.1109/ICoICI62503.2024.10696263(1-7)Online publication date: 28-Aug-2024
https://doi.org/10.1109/ICoICI62503.2024.10696263
Goindi SThakur KSingh Kapoor D(2024)A Comparative Analysis of Machine Learning and Deep Learning Algorithms for Skin Cancer Detection and Classification: Exploring Performance Across Multiple Parameters2024 3rd International Conference on Sentiment Analysis and Deep Learning (ICSADL)10.1109/ICSADL61749.2024.00018(76-82)Online publication date: 13-Mar-2024
https://doi.org/10.1109/ICSADL61749.2024.00018
Kiran Kolachina SAgada RYan J(2024)Boosting Skin Cancer Detection Accuracy: A Hierarchical Ensemble Approach with Feature Fusion2024 IEEE International Conference on Big Data (BigData)10.1109/BigData62323.2024.10825039(6047-6057)Online publication date: 15-Dec-2024
https://doi.org/10.1109/BigData62323.2024.10825039
A JA UV RA S(2024)A novel CNN framework for skin disease classification using adaptive percentage filter for image binarization and fast-marching inpainting methodMultimedia Tools and Applications10.1007/s11042-023-17967-283:23(63547-63570)Online publication date: 9-Jan-2024
https://doi.org/10.1007/s11042-023-17967-2
Saghir USingh SHasan M(2024)Skin Cancer Image Segmentation Based on Midpoint Analysis ApproachJournal of Imaging Informatics in Medicine10.1007/s10278-024-01106-w37:5(2581-2596)Online publication date: 16-Apr-2024
https://doi.org/10.1007/s10278-024-01106-w
Sivakumar AVedhapriyavadhana RGanapathy S(2024)An intelligent skin cancer detection system using two-level multi-column convolutional neural network architectureNeural Computing and Applications10.1007/s00521-024-10252-9Online publication date: 2-Aug-2024
https://doi.org/10.1007/s00521-024-10252-9
Zakariah MAl-Razgan MAlfakih T(2024)Skin cancer detection with MobileNet-based transfer learning and MixNets for enhanced diagnosisNeural Computing and Applications10.1007/s00521-024-10227-w36:34(21383-21413)Online publication date: 1-Dec-2024
https://dl.acm.org/doi/10.1007/s00521-024-10227-w
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

HTML Format

View this article in HTML Format.

Figures

Tables

Media

View Table of Conten