Tuberculosis (TB) and lung cancer remain leading causes of mortality worldwide, emphasizing the need for reliable automated diagnostic systems. Existing deep learning approaches typically address segmentation and classification as independent tasks or rely on loosely coupled hybrid architectures, limiting joint optimization and interpretability. To address these limitations, this work proposes a Dual-Path ConvNet–Vision Transformer (ViT) hybrid framework for simultaneous pulmonary disease classification and lesion segmentation. Unlike fully shared multi-task models, the proposed design integrates convolutional feature extraction for classification with transformer-based global context modeling for segmentation, followed by feature-level fusion to enhance diagnostic consistency. The framework is evaluated on the IQ-OTH/NCCD dataset, achieving an accuracy of 0.950, recall of 0.951, precision of 0.947, specificity of 0.975, F1-score of 0.949, and AUC of 0.963. Results demonstrate that the proposed hybrid approach provides robust and interpretable performance for pulmonary disease co-analysis while maintaining architectural flexibility.

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.