Efficient and Accurate Brain Tumor Diagnosis: A CNN Approach for MRI Image Classification

This study uses CNN to detect and classify brain tumors from MRI data automatically. Due to tumor diversity and location, brain tumor diagnosis is difficult. Diagnostics are slow, subjective, and require expertise. This automated approach aims to address these limitations. The proposed model significantly improves diagnostic accuracy and efficiency, achieving a classification accuracy of 99.32%. By providing fast and reliable results, the system supports health care professionals in making informed clinical decisions and optimizes resource utilization for precise and timely patient care.