MAGnet: Multiscale Attention Guided Network for Enhanced Road Extraction from Satellite Imagery Nomaiya Bashree, Tareque Bashar Ovi, Hussain Nyeem, Md Abdul Wahed, Faiaz Hasanuzzaman Rhythm, et al. Iet Image Processing, 2026 Efficient extraction of roads from high‐resolution satellite images is critical for urban planning, disaster management and autonomous navigation, especially in complex urban environments. Existing segmentation techniques require significant manual effort and are prone to low accuracy, algorithms based on convolutional neural networks, such as U‐Net improve upon this. Still, their symmetrical encoder–decoder design fails to capture multi‐scale features, suffers from poor gradient flow and creates a semantic gap between encoded and decoded features. To mitigate these issues, we present MAGnet, a multiscale attention guided network that enhances road extraction by incorporating an attention guided regional feature block for multiscale feature fusion, employing squeeze and excitation for channel refinement, and addressing overfitting in conventional U‐shaped architectures. MAGnet integrates a focus gate system in skip connections to mitigate vanishing gradients and feature redundancy, alongside a tri‐level attention unit to bridge the disparity in information representation between the encoder and decoder through channel, spatial and pixel‐level attention. MAGnet achieves improved performance on benchmark datasets like Massachusetts Roads and DeepGlobe, with a more than 5% increase in dice coefficient and a 3% rise in mean intersection over union over top models. Its computational efficiency is underscored by a parameter count of 14.22M, 55.76 Giga floating‐point operations and 27.86 Giga multiply‐accumulate operations. Furthermore, MAGnet's decision‐making is enhanced by explainable artificial intelligence techniques for better interpretability. These results suggest that MAGnet offers a computationally efficient and interpretable approach to road extraction from high‐resolution satellite imagery.
SEA-Net: Dual Attention U-Net for Bleeding Segmentation in Capsule Endoscopy Images Tareque Bashar Ovi, Nomaiya Bashree, Hussain Nyeem, Md Abdul Wahed, Faiaz Hasanuzzaman Rhythm, et al. International Journal of Imaging Systems and Technology, 2026 Gastrointestinal (GI) bleeding, arising from various conditions, can be critical if untreated. Wireless capsule endoscopy (WCE) is a highly effective method for detecting GI bleeding, offering full visualization of the GI tract. However, the large number of images generated per patient poses challenges for clinicians, leading to prolonged analysis times and increased risk of human error. This emphasizes the need for computer‐aided diagnosis systems. In this study, we introduce SEA‐Net ( S tructured E fficient A ttention Net work), a novel deep learning network for detecting bleeding regions in WCE images. SEA‐Net integrates a Convolutional Block Attention Module (CBAM) with long skip connections to enhance gradient flow and improve blood region localization. The EfficientNet‐B4 encoder improves feature extraction efficiency and generalizability. A five‐fold cross validation demonstrates consistent performance, while generalization tests, including precision‐recall curves, ROC curves, and F1 measure, further validate the model's robustness. Minimal performance degradation was observed when the training data was reduced from 80% to 20%. Experimental results show that SEA‐Net achieves a Dice score of 93.64% and an IoU score of 88.61% on a publicly available WCE dataset, outperforming state‐of‐the‐art models and highlighting its strong potential for clinical application.
Multi-Level Dual Pixel Value Ordering based High-Capacity Reversible Data Hiding Md Abdul Wahed, Hussain Nyeem 2025 International Conference on Electrical Computer and Communication Engineering Ecce 2025, 2025 We introduce a high-capacity Reversible Data Hiding (RDH) scheme with multi-level dual Pixel Value Ordering (DPVO) approach. The DPVO process operates in two phases: forward and backward. While conventional PVO-based embedding is applied to image blocks in the forward phase, the backward phase’s refined embedding uses maximum and minimum pixel sets to apply PVO with PEE selectively, allowing partial restoration of original pixel values. By employing the DPVO approach over the image blocks in horizontal and vertical directions in the successive levels, we exploit the inherent pixel correlations within images, substantially enhancing the data embedding capacity with highly maintained image quality. The proposed scheme demonstrates competitive image quality at low embedding rates and excels in achieving significantly high capacity with minimal quality loss, making it ideal for applications requiring high-capacity data embedding like metadata embedding, digital watermarking, and electronic records, etc., where maintaining image fidelity is also crucial.
