Precision in Neuro-Oncology a Binary-Multiclass Cascade for MRI-Based Tumor Diagnosis

This paper presents a unique two-stage framework for automated brain tumor identification based on Magnetic Resonance Imaging (MRI) data. The suggested approach combines binary detection and multiclass classification to improve both accuracy and computing efficiency. In the first stage, DenseNet-169 is used to detect the existence of a tumor with high sensitivity while minimizing false negatives. MRI scans that are tumor-positive proceed to the second stage, when InceptionResNetV2 is used for multiclass classification to distinguish between glioma, meningioma, pituitary, metastatic, and normal categories. This cascade technique takes advantage of the unique capabilities of both architectures: DenseNet-169's fast feature reuse improves detection accuracy, whilst InceptionResNetV2's multiscale processing and residual connections excel at collecting complicated patterns for exact classification. A large number of experiments have been performed to test the framework on a range of datasets and it outperformed single-stage models. It shows higher sensitivity, specificity, and overall classification accuracy. The flexibility of the two-stage technique allows for task-specific optimization, reduces the cost of processing, and enhances interpretability. This is a large step forward for automated brain tumor diagnosis, which provides a robust and effective solution for clinical usage. These findings therefore suggest that the proposed system can significantly enhance diagnostic accuracy, allowing for rapid and effective treatment decisions.