AI-Powered Radiomics and Advanced Deep Learning for Precision Brain Tumor Detection: A Next-Generation Approach

In neuro-oncology, accurate brain tumor detection and classification are crucial since a timely diagnosis has a substantial impact on patient outcomes. In traditional model approaches are useful but often insufficient for accurate diagnosis and treatment planning. The latest radiomics and AI technologies were used to construct a brain tumor identification tool. Using a large set of MRI scans annotated by professional radiologists, a CNN-radiomics feature extraction model was trained and tested. This system recorded macroscopic and microscopic tumor features, and multi-scale feature fusion increased its accuracy. The model beat standard machine learning methods, which averaged $\mathbf{8 8 \%}$, with $\mathbf{9 6 \%}$ detection accuracy. Precision-recall analysis showed the model’s clinical reliability with an F1 score of 0.9. Comparisons demonstrated a 30% increase in sensitivity and a 25% decrease in false positives over traditional radiomics methods. The combination of advanced deep learning and AI-powered radiomics has greatly improved the precision of brain tumor identification. This combination supports personalized treatment approaches and may improve patient survival. This model will be integrated into clinical procedures and tested on larger, multi-institutional datasets. The proposed method outperformed traditional machine learning by 8% in detection accuracy.