Author

S. Ahamed

View on Semantic Scholar

2 papers indexed here

We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.

Not the right person? Other researchers publish under this name.

Conference Jul 2026

PAD-Net: Towards Intelligent Diagnosis of Peripheral Artery Disease with a Deep Learning Framework

Peripheral Artery Disease (PAD) is a serious and potentially limb and life-threatening condition that demands a timely and accurate diagnosis to prevent severe complications. Traditional diagnostic methods are often slow and error prone. Although PAD localization remains relatively less explored using machine learning, the development of efficient and deployable models for real-time arterial blockage identification demands more investigation. In this paper, we propose PAD-Net, a novel end-to-end object detection framework specifically optimized for the identification and localization of arterial blockage. By integrating Light-Weight Feature Adapter (LFA) and Transformer block into You Only Look Once (YOLO) architecture, our model enhances the possibility of capturing multi-scale vascular structures often obscured in complex medical backgrounds/conditions. The proposed model was evaluated on a novel dataset of lower-extremity CT angiograms collected from the National Institute of Cardiovascular Disease, Bangladesh. Experimental results demonstrate that the model achieves a Mean Average Precision of 0.87 (mAP@0.5) and 0.423 (mAP@0.5:0.95), achieving competitive performance compared with state-of-theart YOLO and RT-DETR (Real Time Detection Transformer) models. At the same time, it maintains real-time speed to make predictions. The proposed PAD-Net gives a strong and scalable solution for AI-based PAD diagnosis. Furthermore, to validate real-world applicability, the model is deployed on a web-based Streamlit Community Cloud. The deployment of the proposed model highlights the effectiveness of reliable PAD detection and its integration into clinical workflows.

Md Raisul Islam, M. Zaman, S. Ahmmed et al. · International Conference on... · 0 citations

Conference Jul 2026

Governance-Aware, Privacy-Preserving Retrieval-Augmented Large Language Models for Clinical Triage Decision Support

Large Language Models (LLMs) offer significant potential for clinical decision support, but their deployment is constrained by hallucination, privacy risks, and limited interpretability. Retrieval-Augmented Generation (RAG) improves reliability by grounding outputs in patient-specific electronic health records (EHRs); however, it introduces new challenges related to sensitive data exposure. This paper proposes a governanceaware, privacy-preserving RAG framework for clinical triage decision support using the MIMIC-IV-Ext dataset. The framework integrates retrieval-based grounding, differential privacy, explainability, and an agentic orchestration layer within a unified architecture. An $\epsilon$-ledger is introduced to track cumulative privacy expenditure, and a Privacy-Utility-Explainability (PUE) framework is used to systematically evaluate trade-offs. Experimental results demonstrate that the proposed approach improves classification performance (e.g., achieving up to 82% accuracy and 88% AUROC) and reduces under-triage risk, while privacy mechanisms introduce only modest performance degradation. Retrieval-based attribution further enhances interpretability by linking predictions to clinically relevant cases. These results highlight the effectiveness of combining retrieval, privacy, and explainability for developing trustworthy and deployable healthcare AI systems.

A. K. I. Riad, Md Abdul Barek, Hossain Shahriar et al. · International Conference on... · 0 citations