To explore the application value of MRI in the diagnosis of brain glioma (BG), in the study, a deep learning-based multimodal feature fusion model was established, which was then applied in BG classification. 60 BG patients who came to our hospital for treatment were selected as research subjects. (ey all accepted the MRI scan and the enhanced scan, and the MRI results were compared with the pathological results. (e results showed that the sensitivity of the algorithm was above 90%, and the sensitivity to diagnose grade IV glioma was as high as 98.28%; the specificity was above 78%, and the specificity to diagnose grade IV glioma was as high as 95.85%; the detection accuracy was above 95%. (e relative fractional anisotropy (rFA) values of the tumor body were smaller than those of peritumoral edema in both the high-grade group and low-grade group, and the difference was notable (P < 0.05); the relative apparent diffusion coefficients (rADC) values of the peritumoral edema were greater than those of tumor bodies of the same grade in both the high-grade group and the low-grade group, and the difference was notable (P < 0.05); notable differences were noted in the rADC values of tumor bodies between the high-grade group and the low-grade group (P < 0.05) and in the rADC values of the glioma peritumoral edema between the high-grade group and the low-grade group (P < 0.05). In summary, MRI based on deep learning raises the sensitivity, specificity, and accuracy to diagnose BG and can more accurately classify BG pathologically, providing reference for clinical treatment of BG.
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