A CONVOLUTIONAL NEURAL NETWORK APPROACH FOR ACCURATE BRAIN TUMOR DETECTION IN MRI SCANS
Keywords:
Brain Tumor Detection, Convolutional Neural Network (CNN), MRI Imaging, Medical Image Analysis, Deep Learning.Abstract
Accurate detection of brain tumors in MRI scans is critical for effective diagnosis and treatment planning. This paper introduces a convolutional neural network (CNN) model specifically designed to improve detection accuracy and reliability in identifying brain tumors across various types and sizes. By employing an optimized CNN architecture, coupled with comprehensive data preprocessing and augmentation techniques, the model enhances feature extraction and classification capabilities, addressing challenges such as tumor variability and image noise. Experimental results reveal substantial improvements in accuracy, precision, and detection rates, demonstrating the model’s robustness in distinguishing between tumor and non-tumor regions. These advancements highlight the model's potential for real-world clinical application, offering a promising tool to support radiologists and improve diagnostic workflows in neuro-oncology.
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