Brain tumor classification and detection via hybrid alexnet-gru based on deep learning

Abstract

Brain tumors are among the most dangerous types of brain cancer due to their aggressiveness. The development of aberrant brain tissue leads to brain tumors, which pose a major threat to people's health and welfare. Early detection of these malignancies is still fairly challenging, particularly when attempting to distinguish between various kinds including gliomas, meningioma, and pituitary tumors. The complexity of the brain's structure further increases the need for a speedy and accurate identification of irregularities. An innovative approach using a hybrid model that combines AlexNet and GRU (Gated Recurrent Unit) neural networks is presented to identify and characterize brain cancers using MRI data. The MRI images should first be sharpened and denoised using a non-local means filter to ensure the best input data. To avoid overfitting and achieve optimal model performance, the AlexNet architecture employs layers to extract specificfeatures from images. Model complexity and potential for generalization are regulated through hyperparameter modification. The GRU component resolves gradient vanishing in deep networks and uses the softmax activation function to categorize braintumorsinto four distinct classes. According to the findings, the model can categorize and diagnose brain cancers with 97% accuracy, 97.63% precision, a robust 96.78% recall rate, and an impressive 97.25% F1-Score. These findings show that theresearch has the potential to improve patient outcomes and create a more favorable healthcare environment in medical imaging and brain tumor detection.