Automated detection of diabetic retinopathy through image feature extraction

Rao, M. Akshatha; Lamani, Dharmanna; Bhandarkar, Rekha; Manjunath, T. C.

doi:10.1109/icaecc.2014.7002402

Cited by 8 publications

(4 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recently, there are increasing applications about target detection [ 29 ]. There are two mainstream types of algorithms: Two-stage methods: for example, the representation is RCNN algorithms [ 30 ], which uses selective search firstly and then adds CNN network to generate a series of sparse candidate boxes and lastly classifies and regresses these candidate boxes.…”

Section: Related Workmentioning

confidence: 99%

Intelligent Solutions in Chest Abnormality Detection Based on YOLOv5 and ResNet50

Zhang

Sun

et al. 2021

Journal of Healthcare Engineering

View full text Add to dashboard Cite

Computer-aided diagnosis (CAD) has nearly fifty years of history and has assisted many clinicians in the diagnosis. With the development of technology, recently, researches use the deep learning method to get high accuracy results in the CAD system. With CAD, the computer output can be used as a second choice for radiologists and contribute to doctors doing the final right decisions. Chest abnormality detection is a classic detection and classification problem; researchers need to classify common thoracic lung diseases and localize critical findings. For the detection problem, there are two deep learning methods: one-stage method and two-stage method. In our paper, we introduce and analyze some representative model, such as RCNN, SSD, and YOLO series. In order to better solve the problem of chest abnormality detection, we proposed a new model based on YOLOv5 and ResNet50. YOLOv5 is the latest YOLO series, which is more flexible than the one-stage detection algorithms before. The function of YOLOv5 in our paper is to localize the abnormality region. On the other hand, we use ResNet, avoiding gradient explosion problems in deep learning for classification. And we filter the result we got from YOLOv5 and ResNet. If ResNet recognizes that the image is not abnormal, the YOLOv5 detection result is discarded. The dataset is collected via VinBigData’s web-based platform, VinLab. We train our model on the dataset using Pytorch frame and use the mAP, precision, and F1-score as the metrics to evaluate our model’s performance. In the progress of experiments, our method achieves superior performance over the other classical approaches on the same dataset. The experiments show that YOLOv5’s mAP is 0.010, 0.020, 0.023 higher than those of YOLOv5, Fast RCNN, and EfficientDet. In addition, in the dimension of precision, our model also performs better than other models. The precision of our model is 0.512, which is 0.018, 0.027, 0.033 higher than YOLOv5, Fast RCNN, and EfficientDet.

show abstract

Section: Related Workmentioning

confidence: 99%

Intelligent Solutions in Chest Abnormality Detection Based on YOLOv5 and ResNet50

Zhang

Sun

et al. 2021

Journal of Healthcare Engineering

View full text Add to dashboard Cite

show abstract

“…Tables 3 and 4 present the comparative performance assessment of the different DR approaches and respective DR image classification accuracy. In traditional approaches [29], authors applied image features such as contrast, correlation, energy, homogeneity, and entropy from the graylevel co-occurrence matrix (GLCM) of the fundus image. Authors applied these GLCM features for DR fundus image classification.…”

Section: Resultsmentioning

confidence: 99%

“…In addition, they applied the PCA algorithm to reduce feature dimension, which was followed by Naïve Bayes-based classification. In [29], the Hurst exponent was used to estimate the fractal dimension (FD) that was applied for DR purposes. Morphology and texture analysis approach was used in [30] to detect DR features, like blood vessels, hard exudates, etc., in colored fundus images.…”

Section: Literature Surveymentioning

confidence: 99%

Deep-learning-based automatic computer-aided diagnosis system for diabetic retinopathy

2017

View full text Add to dashboard Cite

The high-pace rise in advanced computing and imaging systems has given rise to a new research dimension called computer-aided diagnosis (CAD) system for various biomedical purposes. CAD-based diabetic retinopathy (DR) can be of paramount significance to enable early disease detection and diagnosis decision. Considering the robustness of deep neural networks (DNNs) to solve highly intricate classification problems, in this paper, AlexNet DNN, which functions on the basis of convolutional neural network (CNN), has been applied to enable an optimal DR CAD solution. The DR model applies a multilevel optimization measure that incorporates pre-processing, adaptive-learning-based Gaussian mixture model (GMM)-based concept region segmentation, connected component-analysis-based region of interest (ROI) localization, AlexNet DNN-based highly dimensional feature extraction, principle component analysis (PCA)-and linear discriminant analysis (LDA)-based feature selection, and support-vector-machine-based classification to ensure optimal five-class DR classification. The simulation results with standard KAGGLE fundus datasets reveal that the proposed AlexNet DNN-based DR exhibits a better performance with LDA feature selection, where it exhibits a DR classification accuracy of 97.93% with FC7 features, whereas with PCA, it shows 95.26% accuracy. Comparative analysis with spatial invariant feature transform (SIFT) technique (accuracy-94.40%) based DR feature extraction also confirms that AlexNet DNN-based DR outperforms SIFT-based DR. Keywords Computer-aided diagnosis Á Diabetic retinopathy Á Deep neural network Á AlexNet DNN Á Convolutional neural network Á Gaussian mixture model Á Linear discriminant analysis Á SVM

show abstract

“…Classification Accuracy (%) GLCM + SVM [45] 82.00 SVM + NN [46] 89.60 FCM, NN, shape [47] 93.00 HEDFD [48] 94.60 DWT + PCA [28] 95.00 DNN [3] 96.00 FC7-…”

Section: Techniques With Featuresmentioning

confidence: 99%

Fundus Image Classification Using VGG-19 Architecture with PCA and SVD

et al. 2018

View full text Add to dashboard Cite

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.

show abstract

Automated detection of diabetic retinopathy through image feature extraction

Cited by 8 publications

References 5 publications

Intelligent Solutions in Chest Abnormality Detection Based on YOLOv5 and ResNet50

Intelligent Solutions in Chest Abnormality Detection Based on YOLOv5 and ResNet50

Deep-learning-based automatic computer-aided diagnosis system for diabetic retinopathy

Fundus Image Classification Using VGG-19 Architecture with PCA and SVD

Contact Info

Product

Resources

About