Early detection of diabetes potential using cataract image processing approach

Abstract

Diabetes is a disease characterized by a high level of sugar in the blood. The disease occurs because of a disruption in the metabolic system when insulin is not produced effectively and functions properly. High blood sugar levels, for an extended period of time, can harm a few organ systems, including the heart and kidneys. Moreover, it may cause blindness or death if it is not carefully monitored. Because diabetes symptoms are rarely seen, one of the factors that may cause diabetes is self-awareness. Thus, with Artificial Intelligence, this problem can be solved. Artificial intelligence studies how machines can function like humans. This study implemented a Convolutional Neural Network algorithm with (1) input layer, (2) feature learning layer, (3) classification layer, and (4) output layer as the architecture for AI. The accuracy of the developed AI model was measured from its precision, recall, and f1-score. The results show that the model obtained 90% precision, recall, and f1-score for real-world cases found in two hospitals located in Solo and Yogyakarta, Indonesia. According to the results of the tests, 9 out of 10 patients were correctly predicted as having a high risk of diabetes based on their eye images.

Keywords

• artificial intelligence
• cataracts
• diabetes
• early detection