Projects
Hybrid Quantum-Classical Deep Learning for DFU Classification
Quantum Medical AI
Hybrid Quantum-Classical Deep Learning for DFU Classification - by AVISHKA PRAMUDITHA WARNAKULASURIYA FERNANDO
DFU Dataset
Hybrid Quantum-Classical Deep Learning for DFU Classification - by AVISHKA PRAMUDITHA WARNAKULASURIYA FERNANDO
Hybrid Pipeline
Hybrid Quantum-Classical Deep Learning for DFU Classification - by AVISHKA PRAMUDITHA WARNAKULASURIYA FERNANDO
Models 1-3
Hybrid Quantum-Classical Deep Learning for DFU Classification - by AVISHKA PRAMUDITHA WARNAKULASURIYA FERNANDO
AUC vs F1 Insight
I am Avishka Fernando, a BSc (Hons) Computer Science student at Staffordshire University London. My project explores hybrid quantum-classical deep learning for diabetic foot ulcer image classification. Using 1,055 RGB image patches, I developed and compared three model designs combining ResNet transfer learning with four-qubit variational quantum circuits. The study evaluates accuracy, precision, recall, F1-score and ROC-AUC. A key finding is that high ROC-AUC alone can be misleading, showing why medical AI requires robust multi-metric and noise-aware evaluation.
http://linkedin.com/in/avishka-pramuditha-warnakulasuriya-fernando-122b38282
https://avishkapramudithafernando.github.io/Gradex_2026/#hero
Contact AVISHKA PRAMUDITHA