Journal An interpretable deep learning framework for multi-scale diagnosis of gastrointestinal conditions across adult and pediatric populations

Computer Methods and Programs in Biomedicine Update Q1, 2026

Journal A Generalized Responsible AI Framework for Trustworthy Clinical Prediction: Explainability, Fairness, Performance, and Uncertainty in Alzheimer's Disease Modeling

Healthcare Q2, 2026

Journal Revolutionizing sentiment analysis with generative AI: techniques, trends, and challenges

Multimedia Tools and Applications Q1, 2026

Journal MSFE-GallNet-X: a multi-scale feature extraction-based CNN Model for gallbladder disease analysis with enhanced explainability

BMC Medical Imaging Q1, 2025

This study introduces a domain-adaptive explainable deep learning model utilizing multi-scale feature extraction to improve the classification accuracy of gallbladder diseases from grayscale ultrasound images.

Journal Generative adversarial networks (GANs) in medical imaging: Advancements, applications, and challenges

IEEE Access Q1, 2024

This extensive study explores the vast array of applications of GANs in medical imaging, scrutinizing them within recent research. We analyze prevalent datasets, pre-processing techniques, and provide an in-depth discussion of GAN algorithms, elucidating their respective strengths and limitations.

Journal TuSegNet: A Transformer-Based and Attention-Enhanced Architecture for Brain Tumor Segmentation

IEEE Open Journal of the Computer Society Q1, 2025

TuSegNet is a hybrid CNN-transformer model with ASPP and channel attention for brain tumor segmentation. It achieves Dice scores of 0.895, 0.910, and 0.930 on three datasets, surpassing Swin UNet and TransUNet. Ablation studies confirm the effectiveness of each module, showcasing strong performance and clinical relevance.

Journal Advancements in deep learning architectures for aerial imagery

Intelligent Systems with Applications Q1, 2025

This review explores recent deep learning models—CNNs, RNNs, GANs, and Transformers—for aerial image analysis. It compares their effectiveness in tasks like classification and segmentation, highlights architectural advances, and outlines future research needs for more robust, efficient, and adaptive solutions.

Journal A Physics-Guided Bayesian Neural Network for Sensor Fault Detection in Wind Turbines

IEEE Open Journal of the Computer Society Q1, 2025

This paper presents a PINN-BNN model combining physics-informed learning and Bayesian inference for wind turbine fault detection. Achieving 97.6% accuracy and 0.987 AUC-ROC, it enhances interpretability, reduces false negatives, and outperforms 10 baselines, enabling reliable, efficient predictive maintenance in wind energy systems.

Journal Deep learning for plant disease analysis: Current trends, challenges, and future prospects

Agricultural Communications (Under Review)