FSPBO-DQN: SeGAN based segmentation and Fractional Student Psychology Optimization enabled Deep Q Network for skin cancer detection in IoT applications
作者：
Highlights：
• The highlights of the article are given below for your kind perusal. Kindly, consider and forward my article for further processes.
• In this research, an effective FSPBO-based DQN is designed to detect the skin lesions in IoT network environment.
• The nodes simulated in the network area are allowed to gather the patient images and are routed to the BS.
• The routing is made with the optimization algorithm with the fitness parameters.
• The segmented lesions are used to detect the cancer using DQN, which is tuned by proposed FSPBO algorithm.
• The proposed FSPBO algorithm is designed by integrating the SPBO and FC.
摘要
The highlights of the article are given below for your kind perusal. Kindly, consider and forward my article for further processes.•In this research, an effective FSPBO-based DQN is designed to detect the skin lesions in IoT network environment.•The nodes simulated in the network area are allowed to gather the patient images and are routed to the BS.•The routing is made with the optimization algorithm with the fitness parameters.•The segmented lesions are used to detect the cancer using DQN, which is tuned by proposed FSPBO algorithm.•The proposed FSPBO algorithm is designed by integrating the SPBO and FC.
论文关键词：Internet of Things,Skin cancer detection,Routing,Deep Q Network,Fractional Calculus
论文评审过程：Received 26 October 2021, Revised 22 March 2022, Accepted 4 April 2022, Available online 8 April 2022, Version of Record 6 May 2022.
论文官网地址：https://doi.org/10.1016/j.artmed.2022.102299
