Modified Alexnet architecture for classification of diabetic retinopathy images

Shanthi, T.; Sabeenian, R. S.

doi:10.1016/j.compeleceng.2019.03.004

Cited by 266 publications

(117 citation statements)

References 10 publications

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“…Various studies have also used Probabilistic Neural Network (PNN), Bayesian Classification and Support Vector Machines (SVM) for the classification NPDR and PDR types of diabetic retinopathy. The images of haemorrhages [24] of blood vessels and analysed using image processing techniques and the extracted features when fed into the classifiers help to classify the types of DR diseases [25][26][27].…”

Section: Related Workmentioning

confidence: 99%

Early Detection of Diabetic Retinopathy Using PCA-Firefly Based Deep Learning Model

et al. 2020

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Diabetic Retinopathy is a major cause of vision loss and blindness affecting millions of people across the globe. Although there are established screening methods - fluorescein angiography and optical coherence tomography for detection of the disease but in majority of the cases, the patients remain ignorant and fail to undertake such tests at an appropriate time. The early detection of the disease plays an extremely important role in preventing vision loss which is the consequence of diabetes mellitus remaining untreated among patients for a prolonged time period. Various machine learning and deep learning approaches have been implemented on diabetic retinopathy dataset for classification and prediction of the disease but majority of them have neglected the aspect of data pre-processing and dimensionality reduction, leading to biased results. The dataset used in the present study is a diabetes retinopathy dataset collected from the UCI machine learning repository. At its inceptions, the raw dataset is normalized using the Standardscalar technique and then Principal Component Analysis (PCA) is used to extract the most significant features in the dataset. Further, Firefly algorithm is implemented for dimensionality reduction. This reduced dataset is fed into a Deep Neural Network Model for classification. The results generated from the model is evaluated against the prevalent machine learning models and the results justify the superiority of the proposed model in terms of Accuracy, Precision, Recall, Sensitivity and Specificity.

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Section: Related Workmentioning

confidence: 99%

Early Detection of Diabetic Retinopathy Using PCA-Firefly Based Deep Learning Model

et al. 2020

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“…Table 6 and Figs. 4, 5 and 6 provides a brief comparative study of the DNN-MSO with the existing models such as M-AlexNet [25], AlexNet, VggNet-s, VggNet-16, VggNet-19, GoogleNet and ResNet takes place on the applied same set of DR images. Figure 4 shows the sensitivity analysis of diverse models on the applied dataset.…”

Section: Dataset Descriptionmentioning

confidence: 99%

Deep neural network with moth search optimization algorithm based detection and classification of diabetic retinopathy images

2020

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In recent days, progressive rate of diabetic retinopathy (DR) becomes high and it is needed to develop an automated model for effective diagnosis of DR. This paper presents a new deep neural network with moth search optimization (DNN-MSO) algorithm based detection and classification model for DR images. The presented DNN-MSO algorithm involves different processes namely preprocessing, segmentation, feature extraction and classification. Initially, the contrast level of the DR images is improved using contrast limited adaptive histogram equalization model. After that, the preprocessed images are segmented using histogram approach. Then, Inception-ResNet V2 model is applied for feature extraction. Finally, extracted feature vectors are given to the DNN-MSO based classifier model to classify the different stages of DR. An extensive series of experiments were carried out and the results are validated on Messidor DR dataset. The obtained experimental outcome stated the superior characteristics of the DNN-MSO model by attaining a maximum accuracy, sensitivity and specificity of 99.12%, 97.91% and 99.47% respectively.

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“…And then, the feature vectors of full connection layers with the ReLU in the two-stream network are spliced on the concatenate layer which connects the output of the two feature extraction network, and the fused feature is input to one full connection layers of 1024 nodes coming after the concatenate layer, and the output is fed into a SVM classifier used to map features to a probability distribution of defect classes for the final decision. The final defect type prediction gives 10 classes [30,31].…”

Section: Figure 3 the Two-stream Network Architecturementioning

confidence: 99%

Two-Stream Convolutional Neural Network Based on Gradient Image for Aluminum Profile Surface Defects Classification and Recognition

Duan

Zhang

2020

IEEE Access

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In this paper, a novel two-stream convolutional neural network based on gradient image is performed to effectively classify and identify aluminum profiles defects for the first time. Recent feature fusion methods based on two-stream network prove promising performance for defects classification and recognition. In this paper, we use data enhancement methods to obtain a large number of samples to prevent the over fitting phenomenon in deep learning. The image gradient is calculated with the Sobel operator, and normalized to transform the data between zero and one under the same dimension. We design a two-stream convolutional neural network model adopting Wavelet transform fusion strategy to realize feature fusion on the ReLU6 layer, which uses the original RGB image of aluminum profile and the gradient image corresponding to the original RGB image as inputs to extract features through two sub-networks and fuses features on a concatenate layer to be input into SVM classifier for classification and recognition. Using Bayesian Optimization function and computing the cross-validation classification error to optimize the hyperparameters to choose the best performance configuration is performed. A series of experimental data, which include accuracy and estimated generalized classification errors of single-stream and two-stream networks with different feature fusion strategies on different fusion layers, are conducted and show that the current model has good convergence, accuracy, stability and generalization. On this basis, this paper also proposes a series of innovative methods for the future research of other defects. INDEX TERMS Aluminum profile surface defects, two-stream network, gradient image, convolutional neural network, SVM.

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Modified Alexnet architecture for classification of diabetic retinopathy images

Cited by 266 publications

References 10 publications

Early Detection of Diabetic Retinopathy Using PCA-Firefly Based Deep Learning Model

Early Detection of Diabetic Retinopathy Using PCA-Firefly Based Deep Learning Model

Deep neural network with moth search optimization algorithm based detection and classification of diabetic retinopathy images

Two-Stream Convolutional Neural Network Based on Gradient Image for Aluminum Profile Surface Defects Classification and Recognition

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