Selected publications and research outputs.

Cross-Scale Feature Fusion for Detection Transformers

Details withheld during anonymous peer review

This work investigates a structured feature-fusion approach for Detection Transformer architectures, with a focus on improving multi-scale representation, efficiency, and robustness across object-detection settings.

Explaining Transformer-Based Road Damage Detection

Manuscript under review

This work examines road damage detection as a structurally difficult object detection problem: defects are often small, elongated, visually ambiguous, and inconsistently annotated. It compares CNN- and DETR-family detectors on RDD-2022 and uses explainability analysis to study why transformer-based detectors may better fit the visual and spatial demands of road damage detection.

Spatial feature fusion in 3D convolutional autoencoders for lung tumor segmentation from 3D CT images

Suhail Najeeb, Mohammed Imamul Hassan Bhuiyan

Biomedical Signal Processing and Control, 78

A lung tumor segmentation study introducing spatial feature fusion in 3D convolutional autoencoders to improve volumetric CT segmentation performance.

A Pipeline for Lung Tumor Detection and Segmentation from CT Scans Using Dilated Convolutional Neural Networks

Shahruk Hossain, Suhail Najeeb, Asif Shahriyar, Zaowad R. Abdullah, M. Ariful Haque

ICASSP 2019 - IEEE International Conference on Acoustics, Speech and Signal Processing

An automated pipeline for lung tumor detection and segmentation in 3D CT scans using dilated convolutional neural networks and post-processing.

Classification of Retinal Diseases from OCT scans using Convolutional Neural Networks

Suhail Najeeb, Nowshin Sharmile, Ipsita Sahin, Mohammad Tariqul Islam, Mohammed Imamul Hassan Bhuiyan

2018 10th International Conference on Electrical and Computer Engineering

A retinal OCT classification study using region-of-interest detection and convolutional neural networks to identify retinal abnormalities from scans.