DIAGNOSIS ON LUNG CANCER USING ARTIFICIAL NEURAL NETWORK

Artificial neural networks in the last decade, especially when linked to feedback, have been able to produce complex dynamics in control applications. Although network designs are robust by the ANNs, the more difficult the network design is, the more complex it is. Many investigators tried to automate ANN's computer programs design process. Search and optimization problems can be taken into account as the difficulty of identifying the best network parameter to solve a problem. Two commonly used stochastic genetic algorithms (GA) have recently addressed the problem of optimizing ANN parameters to train different research datasets. The process is optimized using GA to allow the robot to perform complex tasks based on the neural network. However, it cannot always be balanced or successful to use these optimisation algorithms to optimize the ANN training process. These algorithms are designed to develop the synaptic weight, connections, Architecture, and Transfer functions of each neuron, three key components for an ANN at the same time.