Diabetes nephropathy detection with deep learning:

Abstract

This research proposes a deep learning-based technique to classifying diabetic retinopathy that employs three cutting-edge convolutional neural network models: InceptionV3, Xception, and DenseNet201. The models were evaluated on a balanced set of images that included photos classified into five categories: healthy, mild DR, moderate DR, proliferate DR, and severe DR. DenseNet201 had the maximum accuracy of 87%, having excellent precision and recall throughout all classes. With an accuracy rate of 57%, InceptionV3 demonstrated reasonable performance, notably in detecting reasonable and Proliferate DR. Xception outperformed InceptionV3 with a 60% accuracy rate, displaying greater precision and recall in most classes, particularly Normal and Severe DR. The results show that DenseNet201 beats the other models, making it a viable option for diabetic retinopathy identification. This study highlights the potential of deep learning models to improve the accuracy and efficiency of medical image processing, making them an important tool for early identification and management of diabetic retinopathy. It is the most common cause of eyesight among working-age adults globally. Early identification and therapy are critical for avoiding significant vision loss. Traditional techniques of diagnosing DR include expert ophthalmologists manually examining retinal pictures, which may be time-consuming and subjective. With the introduction of deep learning techniques, especially convolutional neural networks (CNNs), automatic image analysis has made great progress. The thorough study covers a variety of investigations, from the examination of varied datasets to the construction of real-time detection algorithms. However, substantial shortcomings continue, including low dataset variety, a need for greater model explainability, and difficulty in real-time implementation.