
Sadia Sultana ChowaCharles Darwin University | CDU · Faculty of Engineering, Health, Science and the Environment
Sadia Sultana Chowa
Bachelor of Engineering
Consultant- Research Assistant at Charles Darwin University
About
6
Publications
1,880
Reads
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30
Citations
Introduction
Sadia Sultana Chowa is a graduate student from Daffodil International University, Bangladesh. Her research interests include health informatics, medical imaging, computer vision, image processing, segmentation, detection & classification, deep learning, machine learning, mesh reconstruction, point cloud, Graph Neural Networks, self-supervision, federated learning, etc.
Publications
Publications (6)
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...
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...
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...
Introduction
Breast cancer stands as the second most deadly form of cancer among women worldwide. Early diagnosis and treatment can significantly mitigate mortality rates.
Purpose
The study aims to classify breast ultrasound images into benign and malignant tumors. This approach involves segmenting the breast's region of interest (ROI) employing a...