An Image Processing-Driven CNN Model for Precise Detection of Brain Tumors in MRI Data
Keywords:
Brain tumor, MRI, CNN, deep learning, image classification, clinical diagnosisAbstract
Brain tumors, characterized by the uncontrolled proliferation of abnormal cells within brain tissue, represent a significant clinical challenge affecting individuals across all age groups. The rapid progression and sensitive anatomical location of such tumors underscore the necessity for prompt and precise diagnostic methodologies. Magnetic Resonance Imaging (MRI) remains the gold standard for non-invasive visualization of intracranial abnormalities, offering high-resolution structural information critical for early tumor detection. This study introduces a customized Convolutional Neural Network (CNN) framework specifically designed for the automated analysis of brain MRI scans to facilitate accurate tumor identification. The proposed model comprises five convolutional layers for deep hierarchical feature extraction, each followed by a max-pooling layer to systematically reduce spatial complexity while retaining essential information. A subsequent Flatten layer and two densely connected layers support robust classification, enhanced through the integration of optimized activation functions and an improved hidden layer topology to accelerate convergence and learning stability. Empirical validation reveals an impressive classification accuracy of 98.6% and a precision rate of 97.8%, with minimal cross-entropy loss. Comparative benchmarking against leading architectures—including Mask R-CNN, AFPNet, Fourier CNN, and YOLOv5—demonstrates the superior performance of the proposed approach, affirming its efficacy for advanced clinical decision support in brain tumor diagnostics.
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