Diabetic retinopathy,aged macular degeneration,glaucoma etc.are widely prevalent ocular pathologies which are irreversible at advanced stages.Machine learning based automated detection of these pathologies facilitate ...Diabetic retinopathy,aged macular degeneration,glaucoma etc.are widely prevalent ocular pathologies which are irreversible at advanced stages.Machine learning based automated detection of these pathologies facilitate timely clinical interventions,preventing adverse outcomes.Ophthalmologists screen these pathologies with fundus Fluorescein Angiography Images(FFA)which capture retinal components featuring diverse morphologies such as retinal vasculature,macula,optical disk etc.However,these images have low resolutions,hindering the accurate detection of ocular disorders.Construction of high resolution images from these images,by super resolution approaches expedites the diagnosis of pathologies with better accuracy.This paper presents a deep learning network for Single Image Super Resolution(SISR)of fundus fluorescein angiography images,modeled on residual learning,gridded interpolation and Swish activation functions.The image prior for this network is constructed by gridded interpolation which provides better image fidelity compared to other priors.Evaluation of the performance of this network and comparative analysis with benchmark architectures,on a standard dataset shows that the proposed network is superior with respect to performance metrics and computational time.展开更多
Today, many eye diseases jeopardize our everyday lives, such as Diabetic Retinopathy (DR), Age-related Macular Degeneration (AMD), and Glaucoma.Glaucoma is an incurable and unavoidable eye disease that damages the vis...Today, many eye diseases jeopardize our everyday lives, such as Diabetic Retinopathy (DR), Age-related Macular Degeneration (AMD), and Glaucoma.Glaucoma is an incurable and unavoidable eye disease that damages the vision ofoptic nerves and quality of life. Classification of Glaucoma has been an active fieldof research for the past ten years. Several approaches for Glaucoma classification areestablished, beginning with conventional segmentation methods and feature-extraction to deep-learning techniques such as Convolution Neural Networks (CNN). Incontrast, CNN classifies the input images directly using tuned parameters of convolution and pooling layers by extracting features. But, the volume of training datasetsdetermines the performance of the CNN;the model trained with small datasets,overfit issues arise. CNN has therefore developed with transfer learning. The primary aim of this study is to explore the potential of EfficientNet with transfer learning for the classification of Glaucoma. The performance of the current workcompares with other models, namely VGG16, InceptionV3, and Xception usingpublic datasets such as RIM-ONEV2 & V3, ORIGA, DRISHTI-GS1, HRF, andACRIMA. The dataset has split into training, validation, and testing with the ratioof 70:15:15. The assessment of the test dataset shows that the pre-trained EfficientNetB4 has achieved the highest performance value compared to other models listedabove. The proposed method achieved 99.38% accuracy and also better results forother metrics, such as sensitivity, specificity, precision, F1_score, Kappa score, andArea Under Curve (AUC) compared to other models.展开更多
文摘Diabetic retinopathy,aged macular degeneration,glaucoma etc.are widely prevalent ocular pathologies which are irreversible at advanced stages.Machine learning based automated detection of these pathologies facilitate timely clinical interventions,preventing adverse outcomes.Ophthalmologists screen these pathologies with fundus Fluorescein Angiography Images(FFA)which capture retinal components featuring diverse morphologies such as retinal vasculature,macula,optical disk etc.However,these images have low resolutions,hindering the accurate detection of ocular disorders.Construction of high resolution images from these images,by super resolution approaches expedites the diagnosis of pathologies with better accuracy.This paper presents a deep learning network for Single Image Super Resolution(SISR)of fundus fluorescein angiography images,modeled on residual learning,gridded interpolation and Swish activation functions.The image prior for this network is constructed by gridded interpolation which provides better image fidelity compared to other priors.Evaluation of the performance of this network and comparative analysis with benchmark architectures,on a standard dataset shows that the proposed network is superior with respect to performance metrics and computational time.
文摘Today, many eye diseases jeopardize our everyday lives, such as Diabetic Retinopathy (DR), Age-related Macular Degeneration (AMD), and Glaucoma.Glaucoma is an incurable and unavoidable eye disease that damages the vision ofoptic nerves and quality of life. Classification of Glaucoma has been an active fieldof research for the past ten years. Several approaches for Glaucoma classification areestablished, beginning with conventional segmentation methods and feature-extraction to deep-learning techniques such as Convolution Neural Networks (CNN). Incontrast, CNN classifies the input images directly using tuned parameters of convolution and pooling layers by extracting features. But, the volume of training datasetsdetermines the performance of the CNN;the model trained with small datasets,overfit issues arise. CNN has therefore developed with transfer learning. The primary aim of this study is to explore the potential of EfficientNet with transfer learning for the classification of Glaucoma. The performance of the current workcompares with other models, namely VGG16, InceptionV3, and Xception usingpublic datasets such as RIM-ONEV2 & V3, ORIGA, DRISHTI-GS1, HRF, andACRIMA. The dataset has split into training, validation, and testing with the ratioof 70:15:15. The assessment of the test dataset shows that the pre-trained EfficientNetB4 has achieved the highest performance value compared to other models listedabove. The proposed method achieved 99.38% accuracy and also better results forother metrics, such as sensitivity, specificity, precision, F1_score, Kappa score, andArea Under Curve (AUC) compared to other models.
