Diabetic retinopathy is a critical eye condition that, if not treated, can lead to vision loss. Traditional methods of
diagnosing and treating the disease are time-consuming and expensive. However, machine learning and deep transfer learning (DTL) techniques have shown promise in medical applications, including detecting, classifying, and
segmenting diabetic retinopathy. These advanced techniques offer higher accuracy and performance. ComputerAided Diagnosis (CAD) is crucial in speeding up classification and providing accurate disease diagnoses. Overall,
these technological advancements hold great potential for improving the management of diabetic retinopathy.
The study’s objective was to differentiate between different classes of diabetes and verify the model’s capability
to distinguish between these classes. The robustness of the model was evaluated using other metrics such as
accuracy (ACC), precision (PRE), recall (REC), and area under the curve (AUC). In this particular study, the
researchers utilized data cleansing techniques, transfer learning (TL), and convolutional neural network (CNN)
methods to effectively identify and categorize the various diseases associated with diabetic retinopathy (DR).
They employed the VGG-16CNN model, incorporating intelligent parameters that enhanced its robustness. The
outcomes surpassed the results obtained by the auto enhancement (AE) filter, which had an ACC of over 98%. The
manuscript provides visual aids such as graphs, tables, and techniques and frameworks to enhance understanding.
This study highlights the significance of optimized deep TL in improving the metrics of the classification of the
four separate classes of DR. The manuscript emphasizes the importance of using the VGG16CNN classification
technique in this context.