A Deep Learning-Based Framework for Retinal Disease Classification

Choudhary, Amit; Ahlawat, Savita; Urooj, Shabana; Pathak, Nitish; Lay-Ekuakille, A.; Sharma, Neelam

doi:10.3390/healthcare11020212

Cited by 18 publications

(16 citation statements)

References 35 publications

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“…A confusion matrix is a table for comparing predicted and actual values to measure prediction performance achieved through training [ 29 , 30 ]. As shown in Figure 5 , the rows represent the correct answer class, and the column represents the predicted class.…”

Section: Resultsmentioning

confidence: 99%

Prediction of Cognitive Load from Electroencephalography Signals Using Long Short-Term Memory Network

2023

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In recent years, the development of adaptive models to tailor instructional content to learners by measuring their cognitive load has become a topic of active research. Brain fog, also known as confusion, is a common cause of poor performance, and real-time detection of confusion is a challenging and important task for applications in online education and driver fatigue detection. In this study, we propose a deep learning method for cognitive load recognition based on electroencephalography (EEG) signals using a long short-term memory network (LSTM) with an attention mechanism. We obtained EEG signal data from a database of brainwave information and associated data on mental load. We evaluated the performance of the proposed LSTM technique in comparison with random forest, Adaptive Boosting (AdaBoost), support vector machine, eXtreme Gradient Boosting (XGBoost), and artificial neural network models. The experimental results demonstrated that the proposed approach had the highest accuracy of 87.1% compared to those of other algorithms, including random forest (64%), AdaBoost (64.31%), support vector machine (60.9%), XGBoost (67.3%), and artificial neural network models (71.4%). The results of this study support the development of a personalized adaptive learning system designed to measure and actively respond to learners’ cognitive load in real time using wireless portable EEG systems.

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Section: Resultsmentioning

confidence: 99%

Prediction of Cognitive Load from Electroencephalography Signals Using Long Short-Term Memory Network

2023

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“…The AI-based models have greatly aided ophthalmology in the diagnosis and treatment of retinal disorders. (1,18) The same AI-based algorithms are utilized to recognise eye infections and to assist ophthalmologists in better diagnosing and describing DME, and CNV.…”

Section: Introductionmentioning

confidence: 99%

“…Glaucoma screening using an optic plate and optic cup division technique based on super-pixel characterization has been studied as an algorithm for detecting retinal arteriovenous (AV) scratching. (21,22) Overall, they (18) used Vgg19 deep learning with classification produced an accuracy 99.17% for the model. In the case of (16) achieved accuracy of 97% and 81% of predictive and multiclass classification with Shapely Addictive Explanation (SHAP) for diabetic retinopathy (DR), but they used small datasets of 614 only for training and validation.…”

Section: Introductionmentioning

confidence: 99%

Multiclass Classification and Identification of the External Eye Diseases using Deep CNN

Rashid,

Abate,

Abdi

2024

IJST

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Objective: These eye illnesses can be either internal eye diseases or external eye diseases. The purpose of research is to find the right model for better performance to identify the external eye disease. The model is customised with 16- layers CNN using multiclass classification. Method: The Deep CNN techniques are utilized with multiclass classification, and the model is developed using Vgg16 with different dropout rates of 0.25 and 0.50 to improve accuracy and performance. In this work, a deep convolutional neural network model is proposed to classify and identify external eye diseases like conjunctivitis, blepharitis, and cellulitis. Datasets were taken in 80:20 randomly from blepharitis, cellulitis, and Conjunctivitis to test (242) and train (968) the model after pre-processing. Novelty: The model is novel and unique using deep CNN, Vgg16, and multiclass classification because it has never been classified and predicted previously for external eye disease. Additionally, Vgg16 with dropout rates of 0.25 and 0.50 was not tested. The model is penetrated into fully connected (FC) layers with different dropout rates. Findings: The accuracy of 98.48% and 0.976% for deep CNN and multiclass classification consecutively produced satisfactory results. The efficiency of R2 is evaluated with multiple classes of data that resulted in a range of 0.425 - 0.775 with k = 10 folds. Vgg16 attains the highest performance of 71.54% with changed dropout rates. The effects of fundus in the ocular, such as retinopathy and AMD, can be examined in the future with segmented data using CNN for better optimization. On account of biological changes in eye and retinal structure, models might be constructed or studied. Keywords: Multiclass Classification, Identification, Deep CNN, External Eye Disease, Evaluation

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“…Choudhary et al [ 30 ] proposed a TL technique based on ResNet-50, InceptionV3, and VGG-19 for drusen, choroidal neovascularization, diabetic macular edema, and normal form classification using OCT images. The experiments were performed on a publicly available dataset consisting of 84,568 images, and the model VGG-19 achieved an accuracy of 99.17%.…”

Section: Introductionmentioning

confidence: 99%

Optical Coherence Tomography Image Classification Using Hybrid Deep Learning and Ant Colony Optimization

Khan

Aziz

et al. 2023

Sensors

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Optical coherence tomography (OCT) is widely used to detect and classify retinal diseases. However, OCT-image-based manual detection by ophthalmologists is prone to errors and subjectivity. Thus, various automation methods have been proposed; however, improvements in detection accuracy are required. Particularly, automated techniques using deep learning on OCT images are being developed to detect various retinal disorders at an early stage. Here, we propose a deep learning-based automatic method for detecting and classifying retinal diseases using OCT images. The diseases include age-related macular degeneration, branch retinal vein occlusion, central retinal vein occlusion, central serous chorioretinopathy, and diabetic macular edema. The proposed method comprises four main steps: three pretrained models, DenseNet-201, InceptionV3, and ResNet-50, are first modified according to the nature of the dataset, after which the features are extracted via transfer learning. The extracted features are improved, and the best features are selected using ant colony optimization. Finally, the best features are passed to the k-nearest neighbors and support vector machine algorithms for final classification. The proposed method, evaluated using OCT retinal images collected from Soonchunhyang University Bucheon Hospital, demonstrates an accuracy of 99.1% with the incorporation of ACO. Without ACO, the accuracy achieved is 97.4%. Furthermore, the proposed method exhibits state-of-the-art performance and outperforms existing techniques in terms of accuracy.

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A Deep Learning-Based Framework for Retinal Disease Classification

Cited by 18 publications

References 35 publications

Prediction of Cognitive Load from Electroencephalography Signals Using Long Short-Term Memory Network

Prediction of Cognitive Load from Electroencephalography Signals Using Long Short-Term Memory Network

Multiclass Classification and Identification of the External Eye Diseases using Deep CNN

Optical Coherence Tomography Image Classification Using Hybrid Deep Learning and Ant Colony Optimization

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