<![CDATA[Timely and accurate diagnosis of brain tumors remains a significant challenge in neuro-oncology due to the heterogeneous nature of tumor characteristics and their substantial impact on patient prognosis and treatment outcomes. Conventional diagnostic methods, particularly manual interpretation of medical imaging, often exhibit limited sensitivity and specificity, leading to delayed diagnoses and suboptimal clinical decisions. To address these limitations, this study proposes a tailored Convolutional Neural Network (CNN) framework that leverages hierarchical feature extraction to capture subtle spatial patterns in brain MRI images, offering advantages over traditional machine learning approaches that rely on handcrafted features. This study aims to develop and validate the proposed model to improve the accuracy and efficiency of brain tumor prediction using annotated MRI data. The dataset was systematically preprocessed, augmented, and partitioned into training and testing subsets to ensure reliable evaluation. The proposed CNN architecture introduces a streamlined feature extraction–classification pipeline designed to balance computational efficiency with discriminative capability, making it suitable for limited medical datasets. Experimental results demonstrate that the model achieves an overall classification accuracy of 86.27%, with balanced sensitivity and specificity, representing a measurable improvement over conventional diagnostic workflows and baseline approaches reported in related studies. From a clinical perspective, the model supports early detection by reducing false-negative and false-positive rates, thereby enhancing diagnostic consistency and enabling more timely clinical intervention. These findings highlight the potential of CNN-based systems as fast, accurate, and non-invasive decision-support tools, supporting the integration of artificial intelligence into medical imaging and clinical diagnostic workflows.]]>
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