About
I'm a PhD candidate in Computer Vision and AI at the School of Computer Science and Engineering, UNSW Sydney, developing AI methods for analysing medical imagery — with a current focus on colorectal disease diagnostics.
Before UNSW, I researched Optical Coherence Tomography (OCT) applications in Sri Lanka, completing an MPhil on detecting leaf disease in persimmon via OCT imaging, alongside work on MRI-based brain tumor analysis. My broader interest is building AI systems that make complex medical data faster and more reliable to interpret, in support of better diagnostics and patient outcomes.
Education
BScEng (Hons), Faculty of Engineering, University of Peradeniya
Experience
Casual Academic, School of Computer Science & Engineering, UNSW Sydney
Visiting Lecturer, ICBT Campus
Visiting Lecturer, APIIT
Software Engineer, WSO2
System and Network Engineer (Intern), IBM World Trade Corporation
Research
AI-Driven Analysis of Colorectal Diseases Using Computer Vision and Machine Learning
Developing AI methods to improve the accuracy, efficiency, and interpretability of medical image analysis in colorectal disease diagnostics, at CSE UNSW Sydney under Prof. Arcot Sowmya, Dr. Sonit Singh, and Dr. Praveen Ravindran.
Optical Coherence Tomography (OCT) Applications
Applied OCT and deep learning to early, non-invasive detection of circular leaf spot disease in persimmon (MPhil thesis), at the Faculty of Engineering, University of Sri Jayewardenepura under Dr. Udaya Wijenayake and Dr. Ruchire Eranga Wijesinghe.
MRI-Based Brain Tumor Analysis Using Machine Learning
Classified brain tumors as malignant or benign using demographic and texture features extracted from diffusion-weighted (ADC) MRI, at the University of Peradeniya.
Publications
- Kalupahana, D., Singh, S., Ravindran, P., and Sowmya, A., 2026. Preserving Anatomical Continuity: Three-Stage Pipeline for Colon Segmentation in 3D Abdominal CT Scans. 2026 IEEE 23rd International Symposium on Biomedical Imaging (ISBI). IEEE.
- Kalupahana, D., Kahatapitiya, N.S., Kamalathasan, D., Wijesinghe, R.E., Silva, B.N. and Wijenayake, U., 2024. State-of-the-art of deep learning in multidisciplinary optical coherence tomography applications. IEEE Access.
- Kalupahana, D., Kahatapitiya, N.S., Silva, B.N., Kim, J., Jeon, M., Wijenayake, U. and Wijesinghe, R.E., 2024. Dense convolutional neural network-based deep learning pipeline for pre-identification of circular leaf spot disease of diospyros kaki leaves.
- Kahatapitiya, N.S., Kalupahana, D., Mohamed, H., Silva, B.N., Wijenayake, U., Han, S., Seong, D., Jeon, M., Kim, J. and Wijesinghe, R.E., 2024. Detection of Peak Intensity Using an Integrated Optical Modeling Method for Identifying Defective Apple Leaves. Engineering Proceedings, 82(1), p.45.
- Vijithananda, S.M., Jayatilake, M.L., Gonçalves, T.C., Rato, L.M., Weerakoon, B.S., Kalupahana, T.D., Silva, A.D., Dissanayake, K. and Hewavithana, P.B., 2023. Texture feature analysis of MRI-ADC images to differentiate glioma grades using machine learning techniques. Scientific Reports, 13(1), p.15772.
All publications → Google Scholar