Deep maxout network for lung cancer detection using optimization algorithm in smart Internet of Things

• Published in: Concurrency and computation Vol. 34; no. 25
• Main Authors: Ramkumar, Muthuperumal Periyaperumal, Mano Paul, Pauliah David, Maram, Balajee, Ananth, John Patrick
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 15.11.2022; Wiley Subscription Services, Inc
• Subjects: Algorithms; computer aided diagnosis; Coronaviruses; deep maxout network; Electronic devices; Gas solubility; Global economy; Internet of Things; IoT routing; Lung cancer; lung cancer detection; Nodes; Object recognition; Optimization; Search algorithms; smart IoT; Smart sensors; Swarm intelligence; Viruses
• ISSN: 1532-0626; 1532-0634
• DOI: 10.1002/cpe.7264

Summary The Internet of Things (IoT) has appreciably influenced the technology world in the context of interconnectivity, interoperability, and connectivity using smart objects, connected sensors, devices, data, and appliances. The IoT technology has mainly impacted the global economy, and it extends from industry to different application scenarios, like the healthcare system. This research designed anti‐corona virus‐Henry gas solubility optimization‐based deep maxout network (ACV‐HGSO based deep maxout network) for lung cancer detection with medical data in a smart IoT environment. The proposed algorithm ACV‐HGSO is designed by incorporating anti‐corona virus optimization (ACVO) and Henry gas solubility optimization (HGSO). The nodes simulated in the smart IoT framework can transfer the patient medical information to sink through optimal routing in such a way that the best path is selected using a multi‐objective fractional artificial bee colony algorithm with the help of fitness measure. The routing process is deployed for transferring the medical data collected from the nodes to the sink, where detection of disease is done using the proposed method. The noise exists in medical data is removed and processed effectively for increasing the detection performance. The dimension‐reduced features are more probable in reducing the complexity issues. The created approach achieves improved testing accuracy, sensitivity, and specificity as 0.910, 0.914, and 0.912, respectively.