Header menu link for other important links
Lung cancer prediction using higher-order recurrent neural network based on glowworm swarm optimization
, , Karuppiah M, Islam S.H, Hassan M.M, Fortino G.
Published in Springer Science and Business Media LLC
Volume: 32
Issue: 9
Pages: 4373 - 4386

Therapeutic issues are commonly found in every single person. Tumor is a standout among the most unsafe sicknesses a human can ever had. It is exceptionally hard to distinguish it in its beginning times as its side effects seem just in the progressed stages. Subsequently, the early forecast of lung growth is compulsory for the analysis procedure, and it gives the higher possibilities for fruitful treatment. It is the most difficult approach to upgrade a patient’s possibility for survival. Henceforth, a higher-order neural network system called recurrent neural network with Levenberg–Marquardt model with the help of glowworm swarm optimization algorithm is proposed for managing multimodal disease information. The execution of the proposed strategies is tried with information and the benchmark dataset, and the outcomes demonstrate that the higher-order recurrent neural systems with glowworm swarm optimization give better accuracy of 98% in comparison with customary optimized neural network. © 2018, The Natural Computing Applications Forum.

About the journal
JournalData powered by TypesetNeural Computing and Applications
PublisherData powered by TypesetSpringer Science and Business Media LLC
Open AccessNo