collaborators

7 papers

eess.SP2026

FunnelNet: An End-to-End Deep Learning Framework to Monitor Digital Heart Murmur in Real-Time

Md Jobayer, Md. Mehedi Hasan Shawon, Md Zakir Hossain +4

Heart murmurs are abnormal sounds caused by turbulent blood flow in the heart. Several diagnostic methods are available to detect heart murmurs and their severity, including cardia…

eess.SP2026

Attentive Dilated Convolution for Automatic Sleep Staging using Force-directed Layout

Md Jobayer, Md Mehedi Hasan Shawon, Tasfin Mahmud +2

Sleep stages play an important role in identifying sleep patterns and diagnosing sleep disorders. In this study, we present an automated sleep stage classifier called the Attentive…

cs.LG2025

Interpretable Heart Disease Prediction via a Weighted Ensemble Model: A Large-Scale Study with SHAP and Surrogate Decision Trees

Md Abrar Hasnat, Md Jobayer, Md. Mehedi Hasan Shawon +1

Cardiovascular disease (CVD) remains a critical global health concern, demanding reliable and interpretable predictive models for early risk assessment. This study presents a large…

cs.SD2025

SS-DPPN: A self-supervised dual-path foundation model for the generalizable cardiac audio representation

Ummy Maria Muna, Md Mehedi Hasan Shawon, Md Jobayer +3

The automated analysis of phonocardiograms is vital for the early diagnosis of cardiovascular disease, yet supervised deep learning is often constrained by the scarcity of expert-a…

cs.LG2025

FoundationalECGNet: A Lightweight Foundational Model for ECG-based Multitask Cardiac Analysis

Md. Sajeebul Islam Sk., Md Jobayer, Md Mehedi Hasan Shawon +1

Cardiovascular diseases (CVDs) remain a leading cause of mortality worldwide, underscoring the importance of accurate and scalable diagnostic systems. Electrocardiogram (ECG) analy…

cs.CL2025

CSTRL: Context-Driven Sequential Transfer Learning for Abstractive Radiology Report Summarization

Mst. Fahmida Sultana Naznin, Adnan Ibney Faruq, Mostafa Rifat Tazwar +3

A radiology report comprises several sections, including the Findings and Impression of the diagnosis. Automatically generating the Impression from the Findings is crucial for redu…