Web System Based on Convolutional Neural Networks to Support Early Identification of Ocular Pterygium

This research aligned with SDG No. 9, “Industry, Innovation, and Infrastructure,” as it promotes health and well-being using innovative technologies. The objective was to determine whether the development of a web-based system based on convolutional neural networks improves the early identification of pterygium. Sensitivity, specificity, and accuracy metrics were used to measure the results, yielding excellent incremental values of 96%, 98%, and 97%, respectively. The study was applied research with a quantitative approach and an experimental design, specifically pre-experimental. The study variable was the early identification of ocular pterygium, consisting of a sample of 100 images, which were divided into 50 images corresponding to individuals with ocular pterygium and 50 from healthy individuals. The type of sampling used was non-probabilistic convenience sampling. The results obtained showed an increase in sensitivity of 4.35%, specificity of 2.80%, and accuracy of 3.56%. It is concluded that the proposal positively improves support for the early identification of pterygium, thanks to the high results obtained with the indicators evaluated, which makes it executable and scalable for future research.