An Optimized Genetic Neural Network for Brain Tumour Detection

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Brain tumour classification is an important function of medical research, contributing to timely and accurate treatment decisions. This research uses a convolutional neural network (CNN) optimized by genetic algorithms to classify MRI scanned images of brain tumors into four groups. The CNN algorithm uses hierarchical feature extraction layers, capturing complex tumor-specific patterns greater than traditional methods. By programmatically adjusting genetic algorithm learning rates and dropout rates, the models are normalized and enhanced in real-time to ensure efficient training, reduction of redundancy for the model work well, ensure high levels of input, dealing with changes in brightness and contrast. A grayscale MRI scans dataset was used to train and validate the system, demonstrating the ability to generalize to unseen data obtaining significant classification accuracy Visualization methods including confusion matrices and class-wise accuracy analysis for insights a delve into the predictive capabilities of the genetic algorithm Integration Therapy - highlights a new approach to improve the performance of CNNs in image analysis. The results highspot the capability of such machine learning methods to improve brain tumor diagnostic tools, providing a foundation for future developments in automated healthcare systems.