Computational Methods and Deep Learning for Ophthalmology
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portes grátis
Computational Methods and Deep Learning for Ophthalmology
Hemanth, D. Jude
Elsevier Science & Technology
02/2023
250
Mole
Inglês
9780323954150
15 a 20 dias
Descrição não disponível.
1. Classification of ocular diseases using transfer learning approaches and glaucoma severity grading
D. Selvathi
2. Early diagnosis of diabetic retinopathy using deep learning techniques
Bam Bahadur Sinha, R. Dhanalakshmi and K. Balakrishnan
3. Comparison of deep CNNs in the identification of DME structural changes in retinal OCT scans
N. Padmasini, R. Umamaheswari, Mohamed Yacin Sikkandar and Manavi D. Sindal
4. Epidemiological surveillance of blindness using deep learning approaches
Kurubaran Ganasegeran and Mohd Kamarulariffin Kamarudin
5. Transfer learning-based detection of retina damage from optical coherence tomography images
Bam Bahadur Sinha, Alongbar Wary, R. Dhanalakshmi and K. Balakrishnan
6. An improved approach for classification of glaucoma stages from color fundus images using Efficientnet-b0 convolutional neural network and recurrent neural network
Poonguzhali Elangovan, D. Vijayalakshmi and Malaya Kumar Nath
7. Diagnosis of ophthalmic retinoblastoma tumors using 2.75D CNN segmentation technique
T. Jemima Jebaseeli and D. Jasmine David
8. Fast bilateral filter with unsharp masking for the preprocessing of optical coherence tomography images - an aid for segmentation and classification
Ranjitha Rajan and S.N. Kumar
9. Deep learning approaches for the retinal vasculature segmentation in fundus images
V. Sathananthavathi and G. Indumathi
10. Grading of diabetic retinopathy using deep learning techniques
Asha Gnana Priya H, Anitha J and Ebenezer Daniel
11. Segmentation of blood vessels and identification of lesion in fundus image by using fractional derivative in fuzzy domain
V.P. Ananthi and G. Santhiya
12. U-net autoencoder architectures for retinal blood vessels segmentation
S. Deivalakshmi, R. Adarsh, J. Sudaroli Sandana and Gadipudi Amarnageswarao
13. Detection and diagnosis of diseases by feature extraction and analysis on fundus images using deep learning techniques
Ajantha Devi Vairamani
D. Selvathi
2. Early diagnosis of diabetic retinopathy using deep learning techniques
Bam Bahadur Sinha, R. Dhanalakshmi and K. Balakrishnan
3. Comparison of deep CNNs in the identification of DME structural changes in retinal OCT scans
N. Padmasini, R. Umamaheswari, Mohamed Yacin Sikkandar and Manavi D. Sindal
4. Epidemiological surveillance of blindness using deep learning approaches
Kurubaran Ganasegeran and Mohd Kamarulariffin Kamarudin
5. Transfer learning-based detection of retina damage from optical coherence tomography images
Bam Bahadur Sinha, Alongbar Wary, R. Dhanalakshmi and K. Balakrishnan
6. An improved approach for classification of glaucoma stages from color fundus images using Efficientnet-b0 convolutional neural network and recurrent neural network
Poonguzhali Elangovan, D. Vijayalakshmi and Malaya Kumar Nath
7. Diagnosis of ophthalmic retinoblastoma tumors using 2.75D CNN segmentation technique
T. Jemima Jebaseeli and D. Jasmine David
8. Fast bilateral filter with unsharp masking for the preprocessing of optical coherence tomography images - an aid for segmentation and classification
Ranjitha Rajan and S.N. Kumar
9. Deep learning approaches for the retinal vasculature segmentation in fundus images
V. Sathananthavathi and G. Indumathi
10. Grading of diabetic retinopathy using deep learning techniques
Asha Gnana Priya H, Anitha J and Ebenezer Daniel
11. Segmentation of blood vessels and identification of lesion in fundus image by using fractional derivative in fuzzy domain
V.P. Ananthi and G. Santhiya
12. U-net autoencoder architectures for retinal blood vessels segmentation
S. Deivalakshmi, R. Adarsh, J. Sudaroli Sandana and Gadipudi Amarnageswarao
13. Detection and diagnosis of diseases by feature extraction and analysis on fundus images using deep learning techniques
Ajantha Devi Vairamani
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
ALEXNET; Artery/vein; Big data; Bilateral filter; Blindness; Blood vessel; Classification; Classifier; CNN; Concatenationpath; Convolutional neural network; Convolutional neural network (CNN); Convolutional neural networks; Deep learning; Diabetic maculopathy; Diabetic retinopathy; Diabetic retinopathy (DR); Dilated; EfficientNet-b0; Epidemiology; Feature extraction; Filtering; Fractional derivatives; Fundus image; Fuzzy enhancement; Gaussian filter; Glaucoma; Glaucoma grading; GOOGLENET; Image processing; Inception block; INCEPTION V3; Indian Diabetic Retinopathy Image Dataset (IDRiD); Lesion; Medical image; Medical image processing; Medical image segmentation; Ocular diseases; Processing; Public health; Residual path; ResNet; RESNET50; Retina; Retinal; Retinal blood vessels; Retinal damage detection; Retinoblastoma; Segmentation; Surveillance; Transfer learning; U-net; VGG-16
1. Classification of ocular diseases using transfer learning approaches and glaucoma severity grading
D. Selvathi
2. Early diagnosis of diabetic retinopathy using deep learning techniques
Bam Bahadur Sinha, R. Dhanalakshmi and K. Balakrishnan
3. Comparison of deep CNNs in the identification of DME structural changes in retinal OCT scans
N. Padmasini, R. Umamaheswari, Mohamed Yacin Sikkandar and Manavi D. Sindal
4. Epidemiological surveillance of blindness using deep learning approaches
Kurubaran Ganasegeran and Mohd Kamarulariffin Kamarudin
5. Transfer learning-based detection of retina damage from optical coherence tomography images
Bam Bahadur Sinha, Alongbar Wary, R. Dhanalakshmi and K. Balakrishnan
6. An improved approach for classification of glaucoma stages from color fundus images using Efficientnet-b0 convolutional neural network and recurrent neural network
Poonguzhali Elangovan, D. Vijayalakshmi and Malaya Kumar Nath
7. Diagnosis of ophthalmic retinoblastoma tumors using 2.75D CNN segmentation technique
T. Jemima Jebaseeli and D. Jasmine David
8. Fast bilateral filter with unsharp masking for the preprocessing of optical coherence tomography images - an aid for segmentation and classification
Ranjitha Rajan and S.N. Kumar
9. Deep learning approaches for the retinal vasculature segmentation in fundus images
V. Sathananthavathi and G. Indumathi
10. Grading of diabetic retinopathy using deep learning techniques
Asha Gnana Priya H, Anitha J and Ebenezer Daniel
11. Segmentation of blood vessels and identification of lesion in fundus image by using fractional derivative in fuzzy domain
V.P. Ananthi and G. Santhiya
12. U-net autoencoder architectures for retinal blood vessels segmentation
S. Deivalakshmi, R. Adarsh, J. Sudaroli Sandana and Gadipudi Amarnageswarao
13. Detection and diagnosis of diseases by feature extraction and analysis on fundus images using deep learning techniques
Ajantha Devi Vairamani
D. Selvathi
2. Early diagnosis of diabetic retinopathy using deep learning techniques
Bam Bahadur Sinha, R. Dhanalakshmi and K. Balakrishnan
3. Comparison of deep CNNs in the identification of DME structural changes in retinal OCT scans
N. Padmasini, R. Umamaheswari, Mohamed Yacin Sikkandar and Manavi D. Sindal
4. Epidemiological surveillance of blindness using deep learning approaches
Kurubaran Ganasegeran and Mohd Kamarulariffin Kamarudin
5. Transfer learning-based detection of retina damage from optical coherence tomography images
Bam Bahadur Sinha, Alongbar Wary, R. Dhanalakshmi and K. Balakrishnan
6. An improved approach for classification of glaucoma stages from color fundus images using Efficientnet-b0 convolutional neural network and recurrent neural network
Poonguzhali Elangovan, D. Vijayalakshmi and Malaya Kumar Nath
7. Diagnosis of ophthalmic retinoblastoma tumors using 2.75D CNN segmentation technique
T. Jemima Jebaseeli and D. Jasmine David
8. Fast bilateral filter with unsharp masking for the preprocessing of optical coherence tomography images - an aid for segmentation and classification
Ranjitha Rajan and S.N. Kumar
9. Deep learning approaches for the retinal vasculature segmentation in fundus images
V. Sathananthavathi and G. Indumathi
10. Grading of diabetic retinopathy using deep learning techniques
Asha Gnana Priya H, Anitha J and Ebenezer Daniel
11. Segmentation of blood vessels and identification of lesion in fundus image by using fractional derivative in fuzzy domain
V.P. Ananthi and G. Santhiya
12. U-net autoencoder architectures for retinal blood vessels segmentation
S. Deivalakshmi, R. Adarsh, J. Sudaroli Sandana and Gadipudi Amarnageswarao
13. Detection and diagnosis of diseases by feature extraction and analysis on fundus images using deep learning techniques
Ajantha Devi Vairamani
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
ALEXNET; Artery/vein; Big data; Bilateral filter; Blindness; Blood vessel; Classification; Classifier; CNN; Concatenationpath; Convolutional neural network; Convolutional neural network (CNN); Convolutional neural networks; Deep learning; Diabetic maculopathy; Diabetic retinopathy; Diabetic retinopathy (DR); Dilated; EfficientNet-b0; Epidemiology; Feature extraction; Filtering; Fractional derivatives; Fundus image; Fuzzy enhancement; Gaussian filter; Glaucoma; Glaucoma grading; GOOGLENET; Image processing; Inception block; INCEPTION V3; Indian Diabetic Retinopathy Image Dataset (IDRiD); Lesion; Medical image; Medical image processing; Medical image segmentation; Ocular diseases; Processing; Public health; Residual path; ResNet; RESNET50; Retina; Retinal; Retinal blood vessels; Retinal damage detection; Retinoblastoma; Segmentation; Surveillance; Transfer learning; U-net; VGG-16
