SmartKC++: Improving Performance of Smartphone-Based Corneal Topographers
Abstract
Keratoconus an ocular condition marked by progressive corneal thinning and outward bulging presents diagnostic challenges due to the high cost and lack of portability in conventional corneal topographers. These limitations restrict accessibility for many necessitating affordable and mobile alternatives. Innovations like SmartKC offer a low-cost and portable alternative however there still remains some gaps in performance when compared to commercial topographers. In this paper we introduce SmartKC++ a series of innovative methodological improvements to the image processing pipeline of SmartKC aimed at significantly enhancing its diagnostic precision and reliability. Our comprehensive evaluation on a dataset comprising 303 eye images reveals that SmartKC++ boosts the accuracy of automated keratoconus diagnosis by 7.69% relative to SmartKC.
Cite
Text
Ganatra et al. "SmartKC++: Improving Performance of Smartphone-Based Corneal Topographers." Winter Conference on Applications of Computer Vision, 2025.Markdown
[Ganatra et al. "SmartKC++: Improving Performance of Smartphone-Based Corneal Topographers." Winter Conference on Applications of Computer Vision, 2025.](https://mlanthology.org/wacv/2025/ganatra2025wacv-smartkc/)BibTeX
@inproceedings{ganatra2025wacv-smartkc,
title = {{SmartKC++: Improving Performance of Smartphone-Based Corneal Topographers}},
author = {Ganatra, Vaibhav and Gairola, Siddhartha and Joshi, Pallavi and Balasubramaniam, Anand and Murali, Kaushik and Varadharajan, Arivunithi and Mallikarjuna, Bellamkonda and Kwatra, Nipun and Jain, Mohit},
booktitle = {Winter Conference on Applications of Computer Vision},
year = {2025},
pages = {4392-4399},
url = {https://mlanthology.org/wacv/2025/ganatra2025wacv-smartkc/}
}