A deep learning-based MRI automatic detection model for spinal schwannoma and meningioma

Schwannomas (SCH) and meningiomas (MEN), the two most common primary spinal cord tumors, present a clinical diagnostic challenge due to their overlapping clinical and radiological manifestations. To address this, we developed a deep learning-based object detection model for automated detection of these tumors using magnetic resonance imaging (MRI), which could facilitate early diagnosis and alleviate clinical decision-making burdens. Our study retrospectively analyzed MRI scans from 103 pathologically confirmed SCH and MEN cases at a local hospital (July 2015-August 2024). First, we took YOLOv8n as the baseline model, introduced selective kernel fusion (SKFusion) module to replace the feature fusion layer of the original neck part, added recursive gated convolution (gnConv), and then trained the improved feature fusion model (YOLOv8n-SKNeck). The proposed model achieved notable performance metrics: 91.20% mean accuracy, 90.92% mean recall, and 91.03% mean F1-score for SCH/MEN detection. These results demonstrate that our optimized deep learning framework can effectively automate the detection and differential diagnosis of spinal SCH and MEN through MRI analysis. Thus, the novel method holds significant potential for advancing computer-aided diagnosis and facilitating innovative applications in future clinical practice.