Optimizing hyperparameters in convolutional neural networks for enhanced accuracy in brain tumor diagnosis

Convolutional neural network (CNN) models used to diagnose brain tumors are very sensitive to hyperparameter tuning, which influences the accuracy and stability of these models. The hyperparameters affect the complexity of a derived model, spatial resolution, convergence rate, feature extraction, and non-linear on the neural network. So as to maximize essential parameters such as filter size and number, batch size, the amount of layers, stride padding, learning rate, activation functions, and pooling modes; we shall introduce a carefully configured CNN hyperparameter model. We also use two open brain tumor sets of MRI in our work. The first set of 7,025 brain scans of human brains falls into 4 categories: meningioma, pituitary, glioma and No Tumor. The second variables lie in a set of 255 pictures labeled as either yes or no. Our approach generates high-quality results as they show average F1-score and precision, recall of 96, 94.25% and percent accuracy on dataset 1 and an average F1-score and precision, recall of 88 and 87.5 percent accuracy on dataset. Efforts are made to carry out an extensive comparison with the existing methods so that the findings can be proven to be correct and the findings of that comparison states that our approach records a consistently high score as compared to the traditional approaches. Our model is also trained after tuning these critical hyperparameters that improves its performance and strengthen its generalization ability. This improved CNN model will give the medical professionals with a more precise and efficient tool to diagnose brain cancers, thus simplifying their decision-making process.