An Edge Internet of Things Framework for Machine Learning-Based Skin Cancer Detection Models

Karthik Reddy Kanjula; Ashok Raju Datla; Tianjie Chen; Md Faisal Kabir

doi:10.1109/ICMLA58977.2023.00327

An Edge Internet of Things Framework for Machine Learning-Based Skin Cancer Detection Models

Karthik Reddy Kanjula
, Ashok Raju Datla
, Tianjie Chen
, Md Faisal Kabir

School of Science, Engineering & Technology (Harrisburg)

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

2 Scopus citations

Abstract

Skin cancer is one of the most widespread diseases that can be diagnosed through artificial intelligence and computer vision. In recent years, researchers focused on addressing skin cancer at the edge because of enhanced real-time processing capabilities, reduced data vulnerability, and cost-effective hard-ware solutions. Despite the advancements in neural networks and hardware for edge applications, there is still a gap in translating related theoretical findings into practical applications. To bridge this gap, we propose a Internet of Things framework that is lightweight and easily scalable through federated learning. Furthermore, our end-to-end framework could incorporate other CV models and enhance their inference capabilities through edge acceleration. Additionally, we also developed an end-to-end application for mobile devices to detect skin cancer and recommend nearby skin specialists or discussion forums. Our work has paved the road for future machine learning-based edge applications.

Original language	English (US)
Title of host publication	Proceedings - 22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023
Editors	M. Arif Wani, Mihai Boicu, Moamar Sayed-Mouchaweh, Pedro Henriques Abreu, Joao Gama
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2167-2173
Number of pages	7
ISBN (Electronic)	9798350345346
DOIs	https://doi.org/10.1109/ICMLA58977.2023.00327
State	Published - 2023
Event	22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023 - Jacksonville, United States Duration: Dec 15 2023 → Dec 17 2023

Publication series

Name	Proceedings - 22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023

Conference

Conference	22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023
Country/Territory	United States
City	Jacksonville
Period	12/15/23 → 12/17/23

UN SDGs

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

SDG 3 Good Health and Well-being

All Science Journal Classification (ASJC) codes

Artificial Intelligence
Computer Science Applications
Computer Vision and Pattern Recognition

Access to Document

10.1109/ICMLA58977.2023.00327

Cite this

Kanjula, K. R., Datla, A. R., Chen, T., & Kabir, M. F. (2023). An Edge Internet of Things Framework for Machine Learning-Based Skin Cancer Detection Models. In M. Arif Wani, M. Boicu, M. Sayed-Mouchaweh, P. H. Abreu, & J. Gama (Eds.), Proceedings - 22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023 (pp. 2167-2173). (Proceedings - 22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICMLA58977.2023.00327

Kanjula, Karthik Reddy ; Datla, Ashok Raju ; Chen, Tianjie et al. / An Edge Internet of Things Framework for Machine Learning-Based Skin Cancer Detection Models. Proceedings - 22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023. editor / M. Arif Wani ; Mihai Boicu ; Moamar Sayed-Mouchaweh ; Pedro Henriques Abreu ; Joao Gama. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 2167-2173 (Proceedings - 22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023).

@inproceedings{468d4aa9d1d7493bad8c0fc5b898d63d,

title = "An Edge Internet of Things Framework for Machine Learning-Based Skin Cancer Detection Models",

abstract = "Skin cancer is one of the most widespread diseases that can be diagnosed through artificial intelligence and computer vision. In recent years, researchers focused on addressing skin cancer at the edge because of enhanced real-time processing capabilities, reduced data vulnerability, and cost-effective hard-ware solutions. Despite the advancements in neural networks and hardware for edge applications, there is still a gap in translating related theoretical findings into practical applications. To bridge this gap, we propose a Internet of Things framework that is lightweight and easily scalable through federated learning. Furthermore, our end-to-end framework could incorporate other CV models and enhance their inference capabilities through edge acceleration. Additionally, we also developed an end-to-end application for mobile devices to detect skin cancer and recommend nearby skin specialists or discussion forums. Our work has paved the road for future machine learning-based edge applications.",

author = "Kanjula, \{Karthik Reddy\} and Datla, \{Ashok Raju\} and Tianjie Chen and Kabir, \{Md Faisal\}",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023 ; Conference date: 15-12-2023 Through 17-12-2023",

year = "2023",

doi = "10.1109/ICMLA58977.2023.00327",

language = "English (US)",

series = "Proceedings - 22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023",

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

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editor = "\{Arif Wani\}, M. and Mihai Boicu and Moamar Sayed-Mouchaweh and Abreu, \{Pedro Henriques\} and Joao Gama",

booktitle = "Proceedings - 22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023",

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}

Kanjula, KR, Datla, AR, Chen, T & Kabir, MF 2023, An Edge Internet of Things Framework for Machine Learning-Based Skin Cancer Detection Models. in M Arif Wani, M Boicu, M Sayed-Mouchaweh, PH Abreu & J Gama (eds), Proceedings - 22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023. Proceedings - 22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023, Institute of Electrical and Electronics Engineers Inc., pp. 2167-2173, 22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023, Jacksonville, United States, 12/15/23. https://doi.org/10.1109/ICMLA58977.2023.00327

An Edge Internet of Things Framework for Machine Learning-Based Skin Cancer Detection Models. / Kanjula, Karthik Reddy; Datla, Ashok Raju; Chen, Tianjie et al.
Proceedings - 22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023. ed. / M. Arif Wani; Mihai Boicu; Moamar Sayed-Mouchaweh; Pedro Henriques Abreu; Joao Gama. Institute of Electrical and Electronics Engineers Inc., 2023. p. 2167-2173 (Proceedings - 22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023).

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

TY - GEN

T1 - An Edge Internet of Things Framework for Machine Learning-Based Skin Cancer Detection Models

AU - Kanjula, Karthik Reddy

AU - Datla, Ashok Raju

AU - Chen, Tianjie

AU - Kabir, Md Faisal

PY - 2023

Y1 - 2023

N2 - Skin cancer is one of the most widespread diseases that can be diagnosed through artificial intelligence and computer vision. In recent years, researchers focused on addressing skin cancer at the edge because of enhanced real-time processing capabilities, reduced data vulnerability, and cost-effective hard-ware solutions. Despite the advancements in neural networks and hardware for edge applications, there is still a gap in translating related theoretical findings into practical applications. To bridge this gap, we propose a Internet of Things framework that is lightweight and easily scalable through federated learning. Furthermore, our end-to-end framework could incorporate other CV models and enhance their inference capabilities through edge acceleration. Additionally, we also developed an end-to-end application for mobile devices to detect skin cancer and recommend nearby skin specialists or discussion forums. Our work has paved the road for future machine learning-based edge applications.

AB - Skin cancer is one of the most widespread diseases that can be diagnosed through artificial intelligence and computer vision. In recent years, researchers focused on addressing skin cancer at the edge because of enhanced real-time processing capabilities, reduced data vulnerability, and cost-effective hard-ware solutions. Despite the advancements in neural networks and hardware for edge applications, there is still a gap in translating related theoretical findings into practical applications. To bridge this gap, we propose a Internet of Things framework that is lightweight and easily scalable through federated learning. Furthermore, our end-to-end framework could incorporate other CV models and enhance their inference capabilities through edge acceleration. Additionally, we also developed an end-to-end application for mobile devices to detect skin cancer and recommend nearby skin specialists or discussion forums. Our work has paved the road for future machine learning-based edge applications.

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M3 - Conference contribution

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T3 - Proceedings - 22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023

SP - 2167

EP - 2173

BT - Proceedings - 22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023

A2 - Arif Wani, M.

A2 - Boicu, Mihai

A2 - Sayed-Mouchaweh, Moamar

A2 - Abreu, Pedro Henriques

A2 - Gama, Joao

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023

Y2 - 15 December 2023 through 17 December 2023

ER -

Kanjula KR, Datla AR, Chen T, Kabir MF. An Edge Internet of Things Framework for Machine Learning-Based Skin Cancer Detection Models. In Arif Wani M, Boicu M, Sayed-Mouchaweh M, Abreu PH, Gama J, editors, Proceedings - 22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023. Institute of Electrical and Electronics Engineers Inc. 2023. p. 2167-2173. (Proceedings - 22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023). doi: 10.1109/ICMLA58977.2023.00327

An Edge Internet of Things Framework for Machine Learning-Based Skin Cancer Detection Models

Abstract

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UN SDGs

All Science Journal Classification (ASJC) codes

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