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
N1 - Publisher Copyright:
© 2023 IEEE.
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.
UR - https://www.scopus.com/pages/publications/85190135001
UR - https://www.scopus.com/pages/publications/85190135001#tab=citedBy
U2 - 10.1109/ICMLA58977.2023.00327
DO - 10.1109/ICMLA58977.2023.00327
M3 - Conference contribution
AN - SCOPUS:85190135001
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 -
