Abstract

Medical deep learning models have been increasingly used in imaging-based eye cancer detection and severity measurement. Traditionally, specialists identify and grade the severity of eye cancers, like retinoblastoma, by examining 2D images for features and making subjective estimations. However, the processes of annotation and grading can be labor intensive and time consuming. Using deep learning models, computer vision technologies can aid in predicting the severity of eye cancers by providing more accurate and objective measurements. Medical deep learning models are designed to recognize patterns in medical images and quantify the severity of eye cancers by understanding not just the surface features of the images but also the underlying medical information associated with the disease. The models are trained over time by ingesting large datasets of medical images and adjusting the parameters of its algorithm to better recognize lesions in various stages. The deep learning models used in eye cancer detection and severity measurement can provide high-accuracy results, often outperforming even experienced clinicians. In addition, deep learning based systems can be adapted to work with different datasets from different hospitals and offer near real-time detection and diagnostics of retinoblastoma, reducing misdiagnosis and allowing for more precise patient care.