<![CDATA[The increasing prevalence of brain cancer has emerged as a significant global health issue, with brain neoplasms, particularly gliomas, presenting considerable diagnostic and therapeutic obstacles. The timely and precise identification of such tumors is crucial for improving patient outcomes. This investigation explores the advancement of Convolutional Neural Networks (CNNs) for detecting brain tumors using MRI data, incorporating the Whale Optimization Algorithm (WOA) for the automated tuning of hyperparameters. Moreover, two callbacks, ReduceLROnPlateau and early stopping, were utilized to augment training efficacy and model resilience. The proposed model exhibited exceptional performance across all tumor categories. Specifically, the precision, recall, and F1-scores for Glioma were recorded as 0.997, 0.980, and 0.988, respectively; for meningioma, as 0.983, 0.986, and 0.984; for no tumors, as 0.998, 0.998, and 0.998; and for pituitary, as 0.997, 0.997, and 0.997. The mean performance metrics attained were 0.994 for precision, 0.990 for recall, and 0.992 for F1-score. The overall accuracy of the model was determined to be 0.991. Notably, incorporating callbacks within the CNN architecture improved accuracy to 0.994. Furthermore, when synergized with the WOA, the CNN-WOA model achieved a maximum accuracy of 0.996. This advancement highlights the effectiveness of integrating adaptive learning methodologies with metaheuristic optimization techniques. The findings suggest that the model sustains high classification accuracy across diverse tumor types and exhibits stability and robustness throughout training. The amalgamation of callbacks and the Whale Optimization Algorithm significantly bolster CNN performance in classifying brain tumors. These advancements contribute to the development of more reliable diagnostic instruments in medical imaging]]>
