Detection of Retinal Abnormalities Using Machine Learning Methodologies

Saha, Rituparna; Chowdhury, Anirban; Banerjee, Sreeparna; Chatterjee, Tamojit

doi:10.14311/nnw.2018.28.025

Cited by 8 publications

(4 citation statements)

References 18 publications

(26 reference statements)

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“…This comparative analysis of the proposed lesion detection approach with existing techniques reveals that it outperforms other state-of-the-art methods, with the greatest accuracy improvement of 5.90% over Nayak et al [29]. Comparison with other techniques shows accuracy improvement of 3.52%, 2.90% and 3.77% over Priya et al [30], Paing et al [31] and Saha et al [32] respectively.…”

Section: Discussionmentioning

confidence: 78%

Diabetic Retinopathy Lesion Discriminative Diagnostic System for Retinal Fundus Images

Bhardwaj

Jain

Sood

2020

ABE

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Diabetic retinopathy (DR) is the main cause of retinal damage due to uid leakage from blood vessels. Automated diagnostic systems assist the ophthalmologists practice manual lesion detection techniques which are tedious and time-consuming. A Diabetic Retinopathy Lesion Discrimination (DRLD) model is proposed for abnormality identi cation followed by DR lesion detection based on identi cation of DR pathological symptoms. Shape, intensity and gray-level co-occurrence matrix (GLCM) features are extracted from the identi ed lesions, and exhaustive statistical analysis is performed for optimal feature selection. Overall accuracies of 97.9% and 91.5% are obtained using multi-layer perceptron neural network (MLPNN) and support vector machine (SVM) classi ers, respectively, for non-diseased versus diseased fundus image discrimination. MLPNN provides better performance for the fundus image discrimination approach, and further accuracy of 98.9% is obtained for DR lesion detection. When compared with other state-of-the-art techniques, the proposed approach provides better performance with signi cantly less computational complexity. A maximum accuracy improvement of 20.13% in fundus image discrimination and 5.90% in lesion categorization is achieved.

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Section: Discussionmentioning

confidence: 78%

Diabetic Retinopathy Lesion Discriminative Diagnostic System for Retinal Fundus Images

Bhardwaj

Jain

Sood

2020

ABE

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“…The decision tree has been expanded in Figs. 4 [30,31]. In the current paper, we have included KNN classifier.…”

Section: Discussionmentioning

confidence: 99%

“…It is robust to larger databases [25]. Machine learning using Single Layer Perceptron (SLP) algorithm has also been tested and its performance is compared with SVM and NB classifier [31]. It was concluded that SLP and SVM give comparable results and both outperform NB classifier.…”

Section: Introductionmentioning

confidence: 99%

A cybernetic systems approach to abnormality detection in retina images using case based reasoning

2020

Self Cite

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In this paper, we present a retina abnormality classification framework for diabetic retinopathy and age related macular degeneration using content based image retrieval. This is performed in two phases, namely, feature extraction and pattern recognition. In the first phase, image pre-processing and Otsu multi-level thresholding is applied to retina fundus images to extract eleven low level spatial and statistical features. The second phase consists of machine learning based classification with these features using four machine learning classifiers, namely, Naive Bayes classifier, support vector machine, K-nearest neighbour classifier and random forest classifier. It is found that random forest classifier outperforms all the other classifiers for the detection of both bright and dark lesion classes and achieves 94.8% and 95.1% accuracy, respectively with ROC area 0.977 and 0.98, respectively.

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“…SVM works by finding the best hyperplane for dividing two different data classes ∈ −1,1 . The best hyperplane can be formulated in Equation (1) [38].…”

Section: B Support Vector Machine (Svm)mentioning

confidence: 99%

Automated Staging of Diabetic Retinopathy Using Convolutional Support Vector Machine (CSVM) Based on Fundus Image Data

Novitasari,

Fatmawati,

Hendradi

et al. 2023

JOIV : Int. J. Inform. Visualization

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Diabetic Retinopathy (DR) is a complication of diabetes mellitus, which attacks the eyes and often leads to blindness. The number of DR patients is significantly increasing because some people with diabetes are not aware that they have been affected by complications due to chronic diabetes. Some patients complain that the diagnostic process takes a long time and is expensive. So, it is necessary to do early detection automatically using Computer-Aided Diagnosis (CAD). The DR classification process based on these several classes has several steps: preprocessing and classification. Preprocessing consists of resizing and augmenting data, while in the classification process, CSVM method is used. The CSVM method is a combination of CNN and SVM methods so that the feature extraction and classification processes become a single unit. In the CSVM process, the first stage is extracting convolutional features using the existing architecture on CNN. CSVM could overcome the shortcomings of CNN in terms of training time. CSVM succeeded in accelerating the learning process and did not reduce the accuracy of CNN's results in 2 class, 3 class, and 5 class experiments. The best result achieved was at 2 class classification using CSVM with data augmentation which had an accuracy of 98.76% with a time of 8 seconds. On the contrary, CNN with data augmentation only obtained an accuracy of 86.15% with a time of 810 minutes 14 seconds. It can be concluded that CSVM was faster than CNN, and the accuracy obtained was also better to classify DR.

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Detection of Retinal Abnormalities Using Machine Learning Methodologies

Cited by 8 publications

References 18 publications

Diabetic Retinopathy Lesion Discriminative Diagnostic System for Retinal Fundus Images

Diabetic Retinopathy Lesion Discriminative Diagnostic System for Retinal Fundus Images

A cybernetic systems approach to abnormality detection in retina images using case based reasoning

Automated Staging of Diabetic Retinopathy Using Convolutional Support Vector Machine (CSVM) Based on Fundus Image Data

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