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Prince Ebenezer Adjei

Computer Engineering

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About

Prince Ebenezer Adjei is an Engineer and a lecturer in the department of computer engineering, KNUST. He is the head of the biomedical division for connected devices (CoDe) Lab, a research lab within the department of computer engineering. He had his BSc in Biomedical Engineering, MSc in Biometrics from Paris 12 Val de Marne University, Paris and MPhil in Computer Engineering from KNUST. Professionally, Prince has undertaken consultancy services for the purchase, installation and maintenance of several types of medical equipment including for ICU and ophthalmology applications. Prince has trained several clinicians in the use of oxygen therapy equipment, surgical microscopes and therapeutic lasers.He is interested in research covering artificial intelligence; specifically in machine learning and deep learning applications in ophthalmology, and recently in gastroenterology. He has researched outputs and presentations at both local and international conferences. Prince has taught several undergraduate courses from 2016 - 2019. Currently, he is completing his PhD at the University of Electronic Science and Technology in China. His current research interest is about the utility of synthetic data in the context of deep learning for prognostication, diagnosis and therapy of colon cancer.

Research Summary

(inferred from publications by AI)

The researcher's work spans a comprehensive exploration of diverse research areas, encompassing health sciences, social sciences, physical sciences, and more. Their primary focus is on developing and applying advanced imaging and computational techniques across various domains, including medical device segmentation, image classification methods, and AI-driven approaches in cancer detection. The researcher's work reflects a deep commitment to innovative solutions that bridge healthcare, social issues, and the arts (media), demonstrating a broad and interdisciplinary approach to understanding complex phenomena through cutting-edge research.

Research Themes

All Papers

Cost-Effectiveness Analysis of Myopia Progression Interventions in Children(2023)
Gastrointestinal Disease Classification in Endoscopic Images Using Attention-Guided Convolutional Neural Networks(2021)
Examining the effect of synthetic data augmentation in polyp detection and segmentation(2022)
Semi-Supervised Segmentation Framework for Gastrointestinal Lesion Diagnosis in Endoscopic Images(2023)
A study on endoscopy artefact detection based on deep learning(2022)
A study on endoscopy artefact detection based on deep learning(2022)
Early gastric cancer segmentation in gastroscopic images using a co-spatial attention and channel attention based triple-branch ResUnet(2023)
Supervised contrastive learning for gastrointestinal lesions classification in endoscopic images(2022)
Wavelet‐Based Feature Extraction for Efficient High‐Resolution Image Classification(2025)
Diversity in Stable GANs: A Systematic Review of Mode Collapse Mitigation Strategies(2025)
Examining the relationship between human resource management practices and employee creativity: the mediating role of organizational learning capability(2025)
Deep learning for efficient high-resolution image processing: A systematic review(2025)
GAN-Based Synthetic Gastrointestinal Image Generation(2020)
Towards non-invasive biomarkers in colorectal cancer management: A study on integrating radiomics and deep learning-based image processing for tumor-stroma interaction(2024)
Semi-supervised Gastrointestinal Lesion Segmentation using Adversarial Learning(2021)
Automated Segmentation of Ischemic Stroke Lesions in Non-contrast Computed Tomography Images for Enhanced Treatment and Prognosis(2025)
Early esophagus cancer segmentation from gastrointestinal endoscopic images based on U-Net++ model(2023)
Optic cup and optic disc analysis for glaucoma screening using pulse-coupled neural networks and line profile analysis(2018)
Detection of Cracks in Crankshaft Using an Intelligent Audible Sound-Based Non-Destructive Method(2021)
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Collaboration Network

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About This Profile

This profile is generated from publicly available publication metadata and is intended for research discovery purposes. Themes, summaries, and trajectories are inferred computationally and may not capture the full scope of the lecturer's work. For authoritative information, please refer to the official KNUST profile.