Preliminary Diagnostic System for the Classification of Senile Cataract Using Convolutional Neural Networks

Abstract

This study proposed an AI system to diagnose cataracts using Neural Networks. The development of the system was carried out following the agile Scrum framework, including the development of artefacts defined by the Rational Unified Process software development process. 10 sprints were defined to complete the software development with the defined artefacts. Two methods were described to validate the model and the system in general. The precision, recall and F1-Score metrics were also determined to evaluate the performance and effectiveness of the model in diagnosing cataracts. Cataract classification yields 80% of positive cases found and 20% of positive cases not found. The proposed system uses fundus images to diagnose cataracts through the smartphone camera, obtain an automatic diagnosis and report, and assign an ophthalmologist to give the verdict. For the "Normal" classification, 93% of positive cases were found, while 7% were not. The average number of positive cases found is 86%, with 14% of positive cases not found. In all cases, we have a percentage of more than 80%. After obtaining the results using the established indicators, it is deduced that the preliminary diagnosis system can be considered support so that the doctor's activity is more optimal.