: Brain tumor detection is crucial for improving patient outcomes through early
diagnosis and precise treatment planning. This research presents an in-depth study of advanced
methodologies for detecting and classifying brain tumors using cutting-edge imaging techniques
and machine learning algorithms. The study emphasizes magnetic resonance imaging (MRI)
due to its superior contrast resolution in soft tissues, essential for identifying brain anomalies.
Our approach leverages convolutional neural networks (CNNs), a deep learning architecture, for
automated brain tumor detection. The model is trained on an extensive dataset of annotated MRI
scans, employing data augmentation to enhance robustness and accuracy. The CNN architecture
is optimized to extract relevant features and classify different brain tumors, including gliomas,
meningiomas, and pituitary adenomas, with high precision. Performance evaluation is conducted
using metrics such as accuracy, sensitivity, specificity, and the area under the receiver operating
characteristic curve (AUC-ROC) . The CNN-based method demonstrates significant
improvements over traditional techniques, achieving an accuracy exceeding 96%. Additionally,
the incorporation of transfer learning techniques shows promise for adapting the model to
various medical imaging tasks with minimal retraining. This research highlights the critical role
of integrating advanced computational methods with medical imaging to improve the accuracy
and efficiency of brain tumor detection. The findings contribute to enhanced clinical decision-
making and patient care, underscoring the potential for machine learning to revolutionize
medical diagnostics.