21 citations · 35 across the 6 of their papers we have counts for
7 papers
Integrating Local Detail and Global Context: A Dual-Input Multi-Task Learning Framework for Bone Tumor Diagnosis
S. M. Nasif Uddin, Rusab Sarmun, Muhammad E. H. Chowdhury +3
Primary bone tumors are rare but clinically aggressive neoplasms whose diagnosis from radiographs is challenged by heterogeneous morphology, subtle lesion margins, and overlapping…
Tracing 3D Anatomy in 2D Strokes: A Multi-Stage Projection Driven Approach to Cervical Spine Fracture Identification
Fabi Nahian Madhurja, Rusab Sarmun, Muhammad E. H. Chowdhury +3
Cervical spine fractures require rapid and accurate diagnosis, yet automatic CT interpretation remains challenging as subtle injuries must be assessed across large 3D volumes. We a…
AnatomicalNets: A Multi-Structure Segmentation and Contour-Based Distance Estimation Pipeline for Clinically Grounded Lung Cancer T-Staging
Saniah Kayenat Chowdhury, Rusab Sarmun, Muhammad E. H. Chowdhury +4
Accurate tumor staging in lung cancer is crucial for prognosis and treatment planning and is governed by explicit anatomical criteria under fixed guidelines. However, most existing…
CASR-Net: An Image Processing-focused Deep Learning-based Coronary Artery Segmentation and Refinement Network for X-ray Coronary Angiogram
Alvee Hassan, Rusab Sarmun, Muhammad E. H. Chowdhury +4
Early detection of coronary artery disease (CAD) is critical for reducing mortality and improving patient treatment planning. While angiographic image analysis from X-rays is a com…
Machine-agnostic Automated Lumbar MRI Segmentation using a Cascaded Model Based on Generative Neurons
Promit Basak, Rusab Sarmun, Saidul Kabir +5
Automated lumbar spine segmentation is very crucial for modern diagnosis systems. In this study, we introduce a novel machine-agnostic approach for segmenting lumbar vertebrae and…
Self-DenseMobileNet: A Robust Framework for Lung Nodule Classification using Self-ONN and Stacking-based Meta-Classifier
Md. Sohanur Rahman, Muhammad E. H. Chowdhury, Hasib Ryan Rahman +4
In this study, we propose a novel and robust framework, Self-DenseMobileNet, designed to enhance the classification of nodules and non-nodules in chest radiographs (CXRs). Our appr…
Deep learning in computed tomography pulmonary angiography imaging: a dual-pronged approach for pulmonary embolism detection
Fabiha Bushra, Muhammad E. H. Chowdhury, Rusab Sarmun +7
The increasing reliance on Computed Tomography Pulmonary Angiography (CTPA) for Pulmonary Embolism (PE) diagnosis presents challenges and a pressing need for improved diagnostic so…