A Multilevel Deep Feature Selection Framework for Diabetic Retinopathy Image Classification

Automated medical image analysis is an emerging field of research that identifies the disease with the help of imaging technology. Diabetic retinopathy (DR) is a retinal disease that is diagnosed in diabetic patients. Deep neural network (DNN) is widely used to classify diabetic retinopathy from fundus images collected from suspected persons. The proposed DR classification system achieves a symmetrically optimized solution through the combination of a Gaussian mixture model (GMM), visual geometry group network (VGGNet), singular value decomposition (SVD) and principle component analysis (PCA), and softmax, for region segmentation, high dimensional feature extraction, feature selection and fundus image classification, respectively. The experiments were performed using a standard KAGGLE dataset containing 35,126 images. The proposed VGG-19 DNN based DR model outperformed the AlexNet and spatial invariant feature transform (SIFT) in terms of classification accuracy and computational time. Utilization of PCA and SVD feature selection with fully connected (FC) layers demonstrated the classification accuracies of 92.21%, 98.34%, 97.96%, and 98.13% for FC7-PCA, FC7-SVD, FC8-PCA, and FC8-SVD, respectively.

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Feature Selection for Histopathological Image Classification using levy Flight Salp Swarm Optimizer

Recent Patents on Computer Science ◽

10.2174/2213275912666181210165129 ◽

2019 ◽

Vol 12 (4) ◽

pp. 329-337 ◽

Cited By ~ 2

Author(s):

Venubabu Rachapudi ◽

Golagani Lavanya Devi

Keyword(s):

Feature Selection ◽

Image Classification ◽

Feature Selection Method ◽

Selection Method ◽

Lévy Flight ◽

Levy Flight ◽

Local Optima ◽

Histopathological Image ◽

Surf Features ◽

Histopathological Image Classification

Background: An efficient feature selection method for Histopathological image classification plays an important role to eliminate irrelevant and redundant features. Therefore, this paper proposes a new levy flight salp swarm optimizer based feature selection method. Methods: The proposed levy flight salp swarm optimizer based feature selection method uses the levy flight steps for each follower salp to deviate them from local optima. The best solution returns the relevant and non-redundant features, which are fed to different classifiers for efficient and robust image classification. Results: The efficiency of the proposed levy flight salp swarm optimizer has been verified on 20 benchmark functions. The anticipated scheme beats the other considered meta-heuristic approaches. Furthermore, the anticipated feature selection method has shown better reduction in SURF features than other considered methods and performed well for histopathological image classification. Conclusion: This paper proposes an efficient levy flight salp Swarm Optimizer by modifying the step size of follower salp. The proposed modification reduces the chances of sticking into local optima. Furthermore, levy flight salp Swarm Optimizer has been utilized in the selection of optimum features from SURF features for the histopathological image classification. The simulation results validate that proposed method provides optimal values and high classification performance in comparison to other methods.

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An Automated Model using Deep Convolutional Neural Network for Retinal Image Classification to Detect Diabetic Retinopathy

Proceedings of the International Conference on Computing Advancements ◽

10.1145/3377049.3377067 ◽

2020 ◽

Author(s):

Md Sazzad Hossen ◽

Alim Ahmed Reza ◽

Mahbub C. Mishu

Keyword(s):

Neural Network ◽

Diabetic Retinopathy ◽

Convolutional Neural Network ◽

Image Classification ◽

Retinal Image ◽

Deep Convolutional Neural Network

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Diabetic Retinal Grading Using Attention-Based Bilinear Convolutional Neural Network and Complement Cross Entropy

Entropy ◽

10.3390/e23070816 ◽

2021 ◽

Vol 23 (7) ◽

pp. 816

Author(s):

Pingping Liu ◽

Xiaokang Yang ◽

Baixin Jin ◽

Qiuzhan Zhou

Keyword(s):

Neural Network ◽

Image Processing ◽

Diabetic Retinopathy ◽

Convolutional Neural Network ◽

Image Classification ◽

Network Model ◽

Rapid Development ◽

Image Data ◽

Lesion Detection ◽

Great Success

Diabetic retinopathy (DR) is a common complication of diabetes mellitus (DM), and it is necessary to diagnose DR in the early stages of treatment. With the rapid development of convolutional neural networks in the field of image processing, deep learning methods have achieved great success in the field of medical image processing. Various medical lesion detection systems have been proposed to detect fundus lesions. At present, in the image classification process of diabetic retinopathy, the fine-grained properties of the diseased image are ignored and most of the retinopathy image data sets have serious uneven distribution problems, which limits the ability of the network to predict the classification of lesions to a large extent. We propose a new non-homologous bilinear pooling convolutional neural network model and combine it with the attention mechanism to further improve the network’s ability to extract specific features of the image. The experimental results show that, compared with the most popular fundus image classification models, the network model we proposed can greatly improve the prediction accuracy of the network while maintaining computational efficiency.

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