Inception-v3 vs. DenseNet for Automated Detection of Diabetic Retinopathy
Keywords:
Diabetic Retinopathy; Classification; Fundus Image; CNNAbstract
The purpose of this paper is to explore the effectiveness of automated detection methods in diagnosing diabetic retinopathy (DR), a leading cause of vision loss among individuals with diabetes. By leveraging advancements in artificial intelligence and image processing techniques, the study aims to assess the accuracy and efficiency of automated systems in identifying retinopathy, thus enabling early intervention and improved patient outcomes. A comprehensive review of existing literature on automated detection systems for DR was conducted. Various image analysis algorithms, including deep learning approaches and feature extraction techniques, were explored and evaluated based on their performance in detecting retinal abnormalities associated with DR. In this research, we present an Inception-v3 and DenseNet-based automated detection technique for DR using retinal fundus pictures. This work involves the training, evaluation, and comparison of the performance of DenseNet and Inception-v3 convolutional neural networks (CNN) on a publicly available dataset of retinal fundus images. Inception-v3-based classifiers have performed better than DenseNet-based classifiers with the same dataset. While DenseNet achieved classifier accuracy and precision of 89.2% and 89.6%, respectively, Inception-v3 has been able to achieve classifier accuracy of 95.8% and precision of 95.9%. Inception-v3 has also exceeded area under ROC in comparison to DenseNet by 0.3% in two categories. The findings of this study highlight the promising potential of automated detection methods for DR. The integration of automated systems in clinical settings has the potential to enhance early diagnosis, facilitate timely treatment interventions, and improve patient outcomes.
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