Detection of Abnormalities of Retina Due to Diabetic Retinopathy and Age Related Macular Degeneration Using SVM

Chowdhury, Anirban

doi:10.11648/j.cssp.20160501.11

Cited by 3 publications

(4 citation statements)

References 8 publications

(9 reference statements)

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“…This paper extends our previous works [18][19][20]27] where only DR related abnormalities were detected using Fuzzy c means clustering approach. In this paper, a new way is presented to detect both types of DR lesions (dark and bright) and also dry AMD using multilevel thresholding [24].…”

Section: Introductionsupporting

confidence: 72%

“…These were done after image preprocessing from RGB to green channel image, and noise removal, as required. This was followed by classification using k-nearest neighbor [3], rule based supervised learning [8], support vector machine [12,20] and naïve Bayes machine learning [19] classifiers. In [13], a combination of Gaussian mixture model and k-nearest neighbor classifier has been used to extract the lesions using a reduced number of features.…”

Section: Related Workmentioning

confidence: 99%

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Detection of Retinal Abnormalities Using Machine Learning Methodologies

Saha¹,

Chowdhury²,

Banerjee³

et al. 2018

NNW

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This paper presents an algorithm for the design of a computer aided diagnosis system to detect, quantify and classify the lesions of non-proliferative diabetic retinopathy as well as dry age related macular degeneration from the fundus retina images. Symptoms of non-proliferative diabetic retinopathy in images consist of bright lesions like hard exudates, cotton wool spots and dark lesions like microaneurysms, hemorrhages. Dry age related macular degeneration is manifested as a bright lesion called drusen. The proposed system consists of two parts: image processing, where preprocessed gray scale images are segmented to extract candidate lesions using a combination of Gaussian filtering and multilevel thresholding followed by classification of the different lesions in non-proliferative diabetic retinopathy and age related macular degeneration using perceptron, support vector machine and naïve Bayes classifier. From the comparative performance analysis of the classification techniques, it is observed that comparable results are obtained from single layer perceptron and support vector machine and they both outperform naïve Bayes classifier. The classification accuracy of support vector machine classifier for dark lesion class is 97.13 % and the classification accuracy of single layer perceptron for bright lesion class is 95.13 % with optimal feature set.

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Section: Introductionsupporting

confidence: 72%

Section: Related Workmentioning

confidence: 99%

Detection of Retinal Abnormalities Using Machine Learning Methodologies

Saha¹,

Chowdhury²,

Banerjee³

et al. 2018

NNW

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“…Notably, machine learning and deep learning techniques have demonstrated substantial efficacy in medical diagnostics. Machine learning, characterized by the use of manually selected features and algorithms for disease identification, has yielded satisfactory outcomes ( Rajan and Ramesh, 2015 ; Chowdhury and Banerjee, 2016 ; Hosoda et al, 2020 ; Xu et al, 2020 ; Kooner et al, 2022 ; Yang et al, 2023 ). On the other hand, deep learning harnesses convolutional neural networks to automatically extract features from images ( Mirzania et al, 2021 ; Ho et al, 2022 ; Xie et al, 2022 ; Chen et al, 2023 ; Li et al, 2023 ).…”

Section: Introductionmentioning

confidence: 99%

Application and visualization study of an intelligence-assisted classification model for common eye diseases using B-mode ultrasound images

Zhu,

Liu,

et al. 2024

Front. Neurosci.

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AimConventional approaches to diagnosing common eye diseases using B-mode ultrasonography are labor-intensive and time-consuming, must requiring expert intervention for accuracy. This study aims to address these challenges by proposing an intelligence-assisted analysis five-classification model for diagnosing common eye diseases using B-mode ultrasound images.MethodsThis research utilizes 2064 B-mode ultrasound images of the eye to train a novel model integrating artificial intelligence technology.ResultsThe ConvNeXt-L model achieved outstanding performance with an accuracy rate of 84.3% and a Kappa value of 80.3%. Across five classifications (no obvious abnormality, vitreous opacity, posterior vitreous detachment, retinal detachment, and choroidal detachment), the model demonstrated sensitivity values of 93.2%, 67.6%, 86.1%, 89.4%, and 81.4%, respectively, and specificity values ranging from 94.6% to 98.1%. F1 scores ranged from 71% to 92%, while AUC values ranged from 89.7% to 97.8%.ConclusionAmong various models compared, the ConvNeXt-L model exhibited superior performance. It effectively categorizes and visualizes pathological changes, providing essential assisted information for ophthalmologists and enhancing diagnostic accuracy and efficiency.

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“…In recent years, feature extraction methods using traditional machine learning have become a common method for diagnosing ophthalmologic diseases. The pertinent features of the ophthalmologic diseases were manually selected then identified through machine learning (7)(8)(9)(10)(11)(12)(13). Deep learning used convolutional neural networks to automatically extract image features; it obtained satisfactory results in the field of ophthalmology (14)(15)(16)(17)(18)(19)(20)(21)(22)(23).…”

Section: Introductionmentioning

confidence: 99%

Screening of Common Retinal Diseases Using Six-Category Models Based on EfficientNet

Zhu

Wang

et al. 2022

Front. Med.

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PurposeA six-category model of common retinal diseases is proposed to help primary medical institutions in the preliminary screening of the five common retinal diseases.MethodsA total of 2,400 fundus images of normal and five common retinal diseases were provided by a cooperative hospital. Two six-category deep learning models of common retinal diseases based on the EfficientNet-B4 and ResNet50 models were trained. The results from the six-category models in this study and the results from a five-category model in our previous study based on ResNet50 were compared. A total of 1,315 fundus images were used to test the models, the clinical diagnosis results and the diagnosis results of the two six-category models were compared. The main evaluation indicators were sensitivity, specificity, F1-score, area under the curve (AUC), 95% confidence interval, kappa and accuracy, and the receiver operator characteristic curves of the two six-category models were compared in the study.ResultsThe diagnostic accuracy rate of EfficientNet-B4 model was 95.59%, the kappa value was 94.61%, and there was high diagnostic consistency. The AUC of the normal diagnosis and the five retinal diseases were all above 0.95. The sensitivity, specificity, and F1-score for the diagnosis of normal fundus images were 100, 99.9, and 99.83%, respectively. The specificity and F1-score for RVO diagnosis were 95.68, 98.61, and 93.09%, respectively. The sensitivity, specificity, and F1-score for high myopia diagnosis were 96.1, 99.6, and 97.37%, respectively. The sensitivity, specificity, and F1-score for glaucoma diagnosis were 97.62, 99.07, and 94.62%, respectively. The sensitivity, specificity, and F1-score for DR diagnosis were 90.76, 99.16, and 93.3%, respectively. The sensitivity, specificity, and F1-score for MD diagnosis were 92.27, 98.5, and 91.51%, respectively.ConclusionThe EfficientNet-B4 model was used to design a six-category model of common retinal diseases. It can be used to diagnose the normal fundus and five common retinal diseases based on fundus images. It can help primary doctors in the screening for common retinal diseases, and give suitable suggestions and recommendations. Timely referral can improve the efficiency of diagnosis of eye diseases in rural areas and avoid delaying treatment.

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Detection of Abnormalities of Retina Due to Diabetic Retinopathy and Age Related Macular Degeneration Using SVM

Cited by 3 publications

References 8 publications

Detection of Retinal Abnormalities Using Machine Learning Methodologies

Detection of Retinal Abnormalities Using Machine Learning Methodologies

Application and visualization study of an intelligence-assisted classification model for common eye diseases using B-mode ultrasound images

Screening of Common Retinal Diseases Using Six-Category Models Based on EfficientNet

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