A hybrid approach using convolutional neural networks and genetic algorithm to improve of sensing brain tumor prediction

Abstract

Brain tumor is the most constantly diagnosed cancer, and the opinion of the brain is veritably sensitive and complex, which is the subject of numerous studies and inquiries. In computer vision, deep literacy ways, similar as the convolutional neural network (CNN), are employed due to their bracket capabilities using learned point styles and their capability to work with complex images. still, their performance is largely dependent on the network structure and the named optimization system for tuning the network parameters. In this paper, we present new yet effective styles for training convolutional neural networks. The maturity of current state-of-the-art literacy styles for convolutional neural networks are grounded on grade descent. In discrepancy to traditional convolutional neural network training styles, we propose an enhancement by incorporating the inheritable algorithm for brain tumor prediction. Our work involves designing a convolutional neural network model to grease the bracket process, training the model using different optimizers (Adam and the inheritable algorithm), and assessing the model through colorful trials on the brain magnetic resonance imaging (MRI) dataset. We demonstrate that the convolutional neural network model trained using the inheritable algorithm performs as well as the Adam optimizer, achieving a bracket delicacy of 99.5.