Diagnosing lung cancer with the aid of BPN in associate with AFSO-EA. (26th October 2021) - Rahul, C.; Gopikakumari, R.
This work aims at modelling an optimum back propagation network (BPN) model, by reducing the input feature count and by optimising the number of neurons in each layer of the BPN classifier without compromising the accuracy. This work incorporates artificial fish swarm optimisation (AFSO) and evolutionary algorithm (EA) and proposes a hybrid AFSO-EA for reducing the input feature set. This work also configures a BPN model, where the number of neurons in each hidden layer is optimised using the same hybrid method. The investigation results reveal that the proposed hybrid AFSO-EA technique generates a BPN model, which can achieve 97.5% classification accuracy, with much less computational overhead, than the existing methods.