
Md. Rahad Islam BhuiyanCharles Darwin University | CDU · Faculty of Engineering, Health, Science and the Environment
Md. Rahad Islam Bhuiyan
Bachelor of Engineering
Consultant- Research Assistant at Charles Darwin University
About
7
Publications
1,878
Reads
How we measure 'reads'
A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text. Learn more
20
Citations
Introduction
I am working with Charles Darwin University as a Consultant - Research Assistant, specializing in Federated Learning, Graph neural network, 3D Imaging, Point Cloud & Mesh, Geometric Deep Learning, Unsupervised Classification, Unsupervised anomaly detection, 3D Generative adversarial network, Image Pre-processing, 3D Segmenta I have a solid foundation in state of art technologies due to my research career, especially its applicability in the healthcare industry.
Publications
Publications (7)
The identification and early treatment of retinal disease can help to prevent loss of vision. Early diagnosis allows a greater range of treatment options and results in better outcomes. Optical coherence tomography (OCT) is a technology used by ophthalmologists to detect and diagnose certain eye conditions. In this paper, human retinal OCT images a...
This study presents a novel privacy-preserving self-supervised (SSL) framework for COVID-19 classification from lung CT scans, utilizing federated learning (FL) enhanced with Paillier homomorphic encryption (PHE) to prevent third-party attacks during training. The FL-SSL based framework employs two publicly available lung CT scan datasets which are...
for possible open access publication under the terms and conditions of the Creative Commons Attri-bution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). These authors contributed equally to this work. Abstract: Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition that presents 1 significant diagnostic challenge...
Objective
Early diagnosis of breast cancer can lead to effective treatment, possibly increase long-term survival rates, and improve quality of life. The objective of this study is to present an automated analysis and classification system for breast cancer using clinical markers such as tumor shape, orientation, margin, and surrounding tissue. The...
This study proposes a novel approach for breast tumor classification from ultrasound images into benign and malignant by converting the region of interest (ROI) of a 2D ultrasound image into a 3D representation using the point-e system, allowing for in-depth analysis of underlying characteristics. Instead of relying solely on 2D imaging features, t...
COVID-19, pneumonia, and tuberculosis have had a significant effect on recent global health. Since 2019, COVID-19 has been a major factor underlying the increase in respiratory-related terminal illness. Early-stage interpretation and identification of these diseases from X-ray images is essential to aid medical specialists in diagnosis. In this stu...
Purpose
An automated computerized approach can aid radiologists in the early diagnosis of breast cancer. In this study, a novel method is proposed for classifying breast tumors into benign and malignant, based on the ultrasound images through a Graph Neural Network (GNN) model utilizing clinically significant features.
Method
Ten informative featu...