Objective

2

Diabetic retinopathy(DR) is a serious eye disease that causes blindness.The retinal pathological image is an important criterion for diagnosing eye diseases

and the accurate classification of retinal images is a crucial step taken by doctors in developing personalized treatment plans.The automated classification of diabetic retinopathy images has significant clinical values.The traditional image classification methods based on manually extracted features have problems

including complex retinal image processing

discriminative features extraction difficulties

poor classification performance

time-consuming

and difficult objective and consistent diagnoses.In this paper

an improved deep convolutional neural network based on AlexNet and a deep classifier are proposed to realize automated diabetic retinopathy image classification.

Method

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First

training the retinal samples is insufficient because retinal images contain much noise

and the differences between retinal pathological images at adjacent stages are small.These prevent the application of convolutional neural networks in retinal images classification

and retinal images should be preprocessed before they are used as training samples.Preprocessing mainly includes retinal image denoising

enhancement

and normalization.The small number of retinal images and data imbalance at different stages are solved by data enhancement.Second

the feature extraction network is designed based on the network structure of AlexNet.The data distribution is changed during the training process.Hence

a batch normalization layer is introduced before every convolutional layer and fully connected layer of the AlexNet network to produce a new deep network

which we call the BNnet network.The introduction of a batch normalization layer can accelerate the convergence of the network

improve the classification accuracy of the obtained model

and reduce the need for a dropout layer.The BNnet network is a complex deep convolutional neural network

which not only serves as a feature extraction network for retinal images but also effectively suppresses data distribution changes in the training process.In this work

the BNnet network is pre-trained using the ILSVRC2012 dataset based on the transfer learning strategy

and the obtained model is migrated to the enhanced diabetic retinopathy dataset for further study to capture the distinguishing features.Finally

a classifier is designed based on the fully connected layer

which can map the learned deep features to the sample label space.The classifier is composed of the fully connected

ReLU

and dropout layers and is applied in learning to partition a diabetic retinopathy status to no DR

mild DR

moderate DR

severe DR

and proliferative DR.

Result

2

We designed four groups of comparative experiments to fully describe the effects of the different depths of neural network and the different training methods

the introduced batch normalized layer

and data preprocessing of the experimental results.The experimental results show that the more layers the network has

the more features are learned with sufficient training samples

and the classification performance of the pre-trained network is better than the traditional direct training method.Moreover

the proposed BNnet neural network and training method can capture the differences of various stages of diabetic retinopathy with a classification accuracy of up to 93%

outperforming other methods.The introduction of the batch normalization layer can control the data distribution changes during the training process and improve the recognition rate.In the case of insufficient retinal image samples

the adoption of transfer learning and data enhancement strategies is good for extracting deep discriminative features for classification.Hence

a deep classifier that is composed of fully connected layers can accurately distinguish the stage when a retinal image is located

indicating that the obtained deep features based on BNnet and transfer learning can provide suitable information for classifiers to accurately classify retinal images into five categories.

Conclusion

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We use deep learning methods to achieve the automatic classification of retinal images.We also present a new diabetic retinopathy classification framework that mainly benefits from three important components:the image preprocessing stage

the deep features extraction stage that is based on transfer learning strategies and BNnet neural networks

and the stable classification stage.Intensive dropout and ReLU are used to suppress the over-fitting problem of the deep learning algorithm when the training samples are insufficient.Experimental results show that deep features combined with the proposed methods can provide suitable information for the construction of the most accurate prediction mode

predict diabetic retinopathy status

and effectively avoid the limitations of manual feature extraction and image classification.This method has relatively better robustness and generalization

and it can be widely used for various image classification problems.In future studies

we will likely develop a real-time computer-aided diagnosis system for diabetic retina images based on the above mentioned approach.