R Kaur
Media Design School, Auckland University of Technology, Punjabi University, RIMT University, Punjab Technical University
0000-0003-0741-2377
About
Dr Ranpreet Kaur is a Lecturer at the Department of Software Engineering & AI, Media Design School, New Zealand. She completed a Ph.D. in Computer Engineering with a research area in Artificial Intelligence at the Auckland University of Technology New Zealand in 2022 where she researched computer-aided diagnosis of melanoma skin cancer. She fulfilled her Master of Technology and Bachelor of Technology in Computer Science Engineering at the Punjab Technology University of India. Her research interest is mainly in Artificial Intelligence, Machine Learning, Deep Learning, Biomedical image processing, Embedded systems, and Pattern Recognition. She is an active member of IEEE organization and the Engineering in Medicine and Biology Society (EMBS) of IEEE. She has published several research papers in different conferences and journals that can be found at: https://scholar.google.com/citations?user=jmkx_4EAAAAJ&hl=en&oi=ao
Employment
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Auckland University of Technology Research Assisstant2021 - 2022
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Media Design School Lecturer2020 - Present
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Auckland University of Technology Researcher & Teaching Assistant2019 - 2020
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RIMT University Assisstant Professsor2007 - 2018
Education
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Auckland University of Technology Doctor of Philosophy2019 - 2022
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Punjabi University Bachelor's of Education2014 - 2015
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Punjab Technical University M.Tech2007 - 2011
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Punjab Technical University B.Tech2002 - 2016
Projects & Funding
Projects & funding information is unavailable.
Publications (27)
- Deep Convolutional Neural Network for Melanoma Detection using Dermoscopy Images Save
- Deep Learning in Medical Applications: Lesion Segmentation in Skin Cancer Images Using Modified and Improved Encoder-Decoder Architecture Save
- Deep Learning in Medical Applications: Lesion Segmentation in Skin Cancer Images Using Modified and Improved Encoder-Decoder Architecture Save
- From machine learning to deep learning: experimental comparison of machine learning and deep learning for skin cancer image segmentation Save
- Hairlines removal and low contrast enhancement of melanoma skin images using convolutional neural network with aggregation of contextual information Save
- Lesion Border Detection of Skin Cancer Images Using Deep Fully Convolutional Neural Network with Customized Weights Save
- Melanoma Classification Using a Novel Deep Convolutional Neural Network with Dermoscopic Images Save
- Synthetic Images Generation Using Conditional Generative Adversarial Network for Skin Cancer Classification Save
- Automatic Lesion Segmentation Using Atrous Convolutional Deep Neural Networks in Dermoscopic Skin Cancer images Save
- Skin lesion segmentation using an improved framework of encoder-decoder based convolutional neural network Save