Researcher at Metropolitan University
My research interests focus on deep learning and computer vision, particularly medical image segmentation for accurate and automated disease diagnosis. I am interested in developing advanced deep learning architectures, including CNNs, Transformers, and hybrid models, to improve segmentation accuracy and computational efficiency. I aim to explore explainable artificial intelligence (XAI) techniques to enhance model interpretability, transparency, and clinical reliability. I am also interested in addressing challenges such as limited annotated data, class imbalance, and domain generalization in medical imaging. Ultimately, my goal is to develop robust, interpretable, and clinically applicable AI solutions that support medical professionals in early disease detection and decision-making.