To accurately segment brain tumors and grade gliomas using multi-modal MRI data, MRI-Glioma Net was built as a new 3D Res-UNet framework.  The model uses residual learning, multi-scale feature extraction, and attention-enhanced fusion to delineate three heterogeneous tumor subregions: whole tumor (WT), tumor core (TC), and enhancing tumor (ET). It leverages T1, T2, FLAIR, and T1ce sequences.  It uses fused latent features to incorporate a specialized classification head for glioma grading (low-grade vs. high-grade), which improves discriminative capability. A glioma dataset with extensive cross-validation was used for evaluation across multiple institutions.  MRI-Glioma Net outperformed baseline models such as 3D UNet and Res UNet, achieving Dice Similarity Coefficients (DSCs) of 0.94 (WT), 0.90 (TC), and 0.88 (ET), respectively.  An IoU of 0.86, HD95 of 5.4 mm, and a volumetric similarity of 0.95 were all recorded by the model.  In terms of grading, it achieved better results than attention UNet and nn UNet, with 95% accuracy, 94% precision, 93% recall, and 93.5% F1-score.  With a 7.2 GB GPU usage, a 2.1s inference time per volume, and only a 0.9% accuracy loss post-quantization, the model's efficiency metrics demonstrate its lightweight deployment potential.  Additionally, 95% confidence intervals are computed for key metrics to reflect variability across folds. Statistical significance of improvements over baseline models (3D U-Net, Res-UNet, Attention U-Net, nnU-Net) is evaluated using paired statistical tests (e.g., Wilcoxon signed-rank test), confirming that performance gains are not due to random variation. Efficiency is further validated using inference time per volume, GPU memory consumption, and post-quantization performance, ensuring practical deployment feasibility. The anatomical fidelity is shown by qualitative overlays to be superior, and interpretability is improved by Grad-CAM and error maps. MRI-Glioma Net offers a feasible, effective, and interpretable way to classify gliomas and diagnose tumors in real time in clinical settings. MRI-Glioma Net represents a major step forward in neuro-oncology imaging; its strong performance across segmentation and grading tasks suggests it could be useful for pre-surgical planning, prognosis, and monitoring treatment response.

This paper introduces BlinkFusion, a real-time, interpretable, and detector-independent pipeline for analyzing blink events. The system finds eye ROIs using YOLOv5 as the main detector and a Haar cascade as a backup that has been calibrated. It then stabilizes the detections using lightweight tracking. A small landmark regressor inside each ROI gives six points to calculate the Eye Aspect Ratio (EAR), which keeps the geometric meaning. An uncertainty-weighted smoother combines pose, landmark, and detector confidence. An online hysteresis state machine with minimum-duration and derivative gates makes blink onsets and offsets. Platt-calibrated and fused detector confidences make strong arbitration possible at a reasonable cost. Performance is assessed on EyeBlinkDB (RGB ≥25 fps, ~720p), using subject-independent 10-fold splits and temporal-IoU matching. The model achieves Precision 0.928 ± 0.008, Recall 0.945 ± 0.008, F1 0.936 ± 0.008, AP@tIoU=0.3 = 0.962 ± 0.006, with timing precision of 28.1 ± 1.7 ms onset MAE and 37.1 ± 2.3 ms offset MAE. Condition stratification validates robustness: The values for Bright/Dim F1 are 0.947/0.922, for Frontal/±15°/≥30° yaw F1 they are 0.952/0.936/0.903, for Glasses (No/Yes) they are 0.946/0.925, and for Occlusion (No/Yes) they are 0.948/0.906. With adaptive scheduling (YOLO invocation rate ρ≈0.045), pipeline runs at about 32 FPS on the CPU (about 90 FPS on the observed frame loop) with a fixed 3-frame delay, which is fast enough for real-time use on cheap hardware. Ablations demonstrate progressive improvements resulting from calibrated fusion, tracking, quality-weighted EMA, and hysteresis (F1: 0.903 → 0.952). The approach is modular (you may switch the detector), doesn't need bounding boxes, and makes judgments that can be explained using EAR traces and thresholds. So, BlinkFusion is a useful, ready-to-use solution for HCI and clinical contexts that need clear, accurate, and quick blink analytics.