Rotator cuff tear (RCT) is a common cause of shoulder pain resulting from tendon or muscle injury. Although magnetic resonance imaging (MRI) is the clinical gold standard for RCT diagnosis, its high cost and limited accessibility often delay treatment. While conventional X-ray imaging sensors are widely available and cost-effective, they lack direct visualization of soft tissues. To maximize the diagnostic utility of radiographic sensor data, we propose a two-stage deep learning framework that leverages indirect indicators, particularly greater tuberosity sclerosis (GTS), to assess RCT severity. In the segmentation stage, a multi-attention–gated U-Net with full-scale skip connections accurately delineates GTS regions from the X-ray sensor images. In the classification stage, a dual-branch convolutional network integrates the acquired sensor data and GTS masks using spatial and channel attention mechanisms to classify RCTs into partial- or full-thickness tears. The segmentation model achieved a Dice coefficient of 0.835 and an accuracy of 0.998, outperforming several state-of-the-art methods. The proposed classification network reached an overall accuracy of 0.941, which surpasses those of previously reported MRI-based and X-ray-based approaches. This framework demonstrates how advanced computational technologies can significantly augment the diagnostic capabilities of standard X-ray sensing systems, enabling accurate and efficient RCT assessment, reducing reliance on MRI, and supporting timely clinical decision-making.