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 [9] 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.

Cloud providers use automated watchdogs or monitors to continuously observe service availability and to proactively report incidents when system performance degrades. Improper monitoring can lead to delays in the detection and mitigation of production incidents, which can be extremely expensive in terms of customer impacts and manual toil from engineering resources. Therefore, a systematic understanding of the pitfalls in current monitoring practices and how they can lead to production incidents is crucial for ensuring continuous reliability of cloud services.