Ranpreet Kaur
Lecturer (Information and computing sciences; Engineering; Biomedical and clinical sciences)
New Zealand
Dr. Ranpreet Kaur is a Lecturer at the Department of Software Engineering & AI, Media Design School, New Zealand. She has more than 15 years of teaching and research experience at various academic institutions. She completed her Ph.D. in Computer Engineering with a research area in Biomedical image processing and Artificial Intelligence at the Auckland University of Technology, New Zealand, in 2022, where she researched computer-aided diagnosis of melanoma skin cancer. She completed her Master of Technology and Bachelor of Technology in Computer Science Engineering at the Punjab Technology University of India. Her research interests are mainly in Artificial Intelligence, Machine Learning, Deep Learning, Biomedical image processing, Embedded systems, and Pattern Recognition. She is an active member of the IEEE organization and the Engineering in Medicine and Biology Society (EMBS) of IEEE. She has published several research papers in different refereed conferences and journals.
Publications
- https://onlinelibrary.wiley.com/doi/abs/10.1002/ima.22699
- https://link.springer.com/chapter/10.1007/978-3-030-72073-5_4
- https://www.mdpi.com/1424-8220/22/3/1134
- https://bmcmedimaging.biomedcentral.com/articles/10.1186/s12880-022-00829-y
- https://ieeexplore.ieee.org/abstract/document/9707291
- https://ojs.aut.ac.nz/rangahau-aranga/article/view/32
- https://ieeexplore.ieee.org/abstract/document/9175391
- https://link.springer.com/chapter/10.1007/978-3-030-72073-5_4
- https://www.sciencedirect.com/science/article/pii/S1746809422001756
- https://ieeexplore.ieee.org/abstract/document/9630512
- Identif Vowels in Punjabi Speech Signal Using Formant Frequencies
- Skin Cancer Detection as Malignant or Benign Using Deep Convolutional Neural Network
- Skin lesion segmentation using an improved framework of encoder‐decoder based convolutional neural network
- Deep Learning Model with Atrous Convolutions for Improving Skin Cancer Classification
- Automatic lesion segmentation using atrous convolutional deep neural networks in dermoscopic skin cancer images
- Hairlines removal and low contrast enhancement of melanoma skin images using convolutional neural network with aggregation of contextual information
- Deep Learning in Medical Applications: Lesion Segmentation in Skin Cancer Images Using Modified and Improved Encoder-Decoder Architecture
- Synthetic Images Generation Using Conditional Generative Adversarial Network for Skin Cancer Classification
- From machine learning to deep learning: experimental comparison of machine learning and deep learning for skin cancer image segmentation
- Palm Recognition Using K-Mean Clustering With Geometrical and Texture Features
- A Hybrid Neural Approach For Character Recognition System
- Deep Convolutional Neural Network for Melanoma Detection using Dermoscopy Images
- Lesion Border Detection of Skin Cancer Images Using Deep Fully Convolutional Neural Network with Customized Weights
- How Machine Learning Can Help with Early Detection of Melanoma
- Melanoma Classification Using a Novel Deep Convolutional Neural Network with Dermoscopic Images