AI in Early Detection of Diabetic Retinopathy
Diabetic retinopathy (DR) is a leading cause of blindness among diabetic patients. Early detection is crucial to prevent severe visual impairment. In the paper “Detection of Diabetic Retinopathy using Deep Learning,” published in the proceedings of the 2023 IEEE 8th International Conference for Convergence in Technology (I2CT), Kenil Patwa, a graduate student in the Data Science MS program at Rutgers University–Camden, collaborated with Dr. Shilpa Bade-Gite, Pooja Bidwai, Kushagra Maheshwari, Kavan Batavia, and Tanmay Singh Bais to develop an automated system for detecting and classifying DR using deep learning models.
The team utilized the APTOS 2019 dataset, containing 3,662 retinal fundus images, to train models such as ResNet-18, GoogLeNet, VGGNet-19, and AlexNet. Their approach focused on feature extraction and classification into multiclass and binary categories. The ResNet-18 model achieved an accuracy of 87% in multiclass classification and 98% in binary classification, demonstrating the potential of deep learning in medical diagnostics.
This research contributes to developing cost-effective, scalable, and accurate diagnostic tools that can assist healthcare professionals in early DR identification, thereby preventing severe visual impairment. The automation of DR detection reduces dependency on manual screening, saving valuable time and resources while minimizing human error.
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