292 related articles for article (PubMed ID: 30109508)
1. An Intelligent Model for Blood Vessel Segmentation in Diagnosing DR Using CNN.
Sangeethaa SN; Uma Maheswari P
J Med Syst; 2018 Aug; 42(10):175. PubMed ID: 30109508
[TBL] [Abstract][Full Text] [Related]
2. Deep Convolutional Neural Network-Based Early Automated Detection of Diabetic Retinopathy Using Fundus Image.
Xu K; Feng D; Mi H
Molecules; 2017 Nov; 22(12):. PubMed ID: 29168750
[TBL] [Abstract][Full Text] [Related]
3. Simple methods for the lesion detection and severity grading of diabetic retinopathy by image processing and transfer learning.
Sugeno A; Ishikawa Y; Ohshima T; Muramatsu R
Comput Biol Med; 2021 Oct; 137():104795. PubMed ID: 34488028
[TBL] [Abstract][Full Text] [Related]
4. Holoentropy enabled-decision tree for automatic classification of diabetic retinopathy using retinal fundus images.
Mane VM; Jadhav DV
Biomed Tech (Berl); 2017 May; 62(3):321-332. PubMed ID: 27514073
[TBL] [Abstract][Full Text] [Related]
5. Development of a Deep Learning Algorithm for Automatic Diagnosis of Diabetic Retinopathy.
Raju M; Pagidimarri V; Barreto R; Kadam A; Kasivajjala V; Aswath A
Stud Health Technol Inform; 2017; 245():559-563. PubMed ID: 29295157
[TBL] [Abstract][Full Text] [Related]
6. Segmentation of retinal blood vessels by a novel hybrid technique- Principal Component Analysis (PCA) and Contrast Limited Adaptive Histogram Equalization (CLAHE).
Sidhu RK; Sachdeva J; Katoch D
Microvasc Res; 2023 Jul; 148():104477. PubMed ID: 36746364
[TBL] [Abstract][Full Text] [Related]
7. Automatic detection of blood vessels in retinal images for diabetic retinopathy diagnosis.
Raja DS; Vasuki S
Comput Math Methods Med; 2015; 2015():419279. PubMed ID: 25810749
[TBL] [Abstract][Full Text] [Related]
8. Referable diabetic retinopathy identification from eye fundus images with weighted path for convolutional neural network.
Liu YP; Li Z; Xu C; Li J; Liang R
Artif Intell Med; 2019 Aug; 99():101694. PubMed ID: 31606108
[TBL] [Abstract][Full Text] [Related]
9. Hemorrhage Detection Based on 3D CNN Deep Learning Framework and Feature Fusion for Evaluating Retinal Abnormality in Diabetic Patients.
Maqsood S; Damaševičius R; Maskeliūnas R
Sensors (Basel); 2021 Jun; 21(11):. PubMed ID: 34205120
[TBL] [Abstract][Full Text] [Related]
10. Automatic Grading of Retinal Blood Vessel in Deep Retinal Image Diagnosis.
Maji D; Sekh AA
J Med Syst; 2020 Sep; 44(10):180. PubMed ID: 32870389
[TBL] [Abstract][Full Text] [Related]
11. DAVS-NET: Dense Aggregation Vessel Segmentation Network for retinal vasculature detection in fundus images.
Raza M; Naveed K; Akram A; Salem N; Afaq A; Madni HA; Khan MAU; Din MZ
PLoS One; 2021; 16(12):e0261698. PubMed ID: 34972109
[TBL] [Abstract][Full Text] [Related]
12. A convolutional neural network for the screening and staging of diabetic retinopathy.
Shaban M; Ogur Z; Mahmoud A; Switala A; Shalaby A; Abu Khalifeh H; Ghazal M; Fraiwan L; Giridharan G; Sandhu H; El-Baz AS
PLoS One; 2020; 15(6):e0233514. PubMed ID: 32569310
[TBL] [Abstract][Full Text] [Related]
13. Detection of retinopathy disease using morphological gradient and segmentation approaches in fundus images.
Toğaçar M
Comput Methods Programs Biomed; 2022 Feb; 214():106579. PubMed ID: 34896689
[TBL] [Abstract][Full Text] [Related]
14. Detection of exudates in fundus photographs using deep neural networks and anatomical landmark detection fusion.
Prentašić P; Lončarić S
Comput Methods Programs Biomed; 2016 Dec; 137():281-292. PubMed ID: 28110732
[TBL] [Abstract][Full Text] [Related]
15. Diabetic Retinopathy (DR) Severity Level Classification Using Multimodel Convolutional Neural Networks.
Abidalkareem AJ; Abd MA; Ibrahim AK; Zhuang H; Altaher AS; Muhamed Ali A
Annu Int Conf IEEE Eng Med Biol Soc; 2020 Jul; 2020():1404-1407. PubMed ID: 33018252
[TBL] [Abstract][Full Text] [Related]
16. Automatic detection of diabetic retinopathy using an artificial neural network: a screening tool.
Gardner GG; Keating D; Williamson TH; Elliott AT
Br J Ophthalmol; 1996 Nov; 80(11):940-4. PubMed ID: 8976718
[TBL] [Abstract][Full Text] [Related]
17. A novel retinal vessel detection approach based on multiple deep convolution neural networks.
Guo Y; Budak Ü; Şengür A
Comput Methods Programs Biomed; 2018 Dec; 167():43-48. PubMed ID: 30501859
[TBL] [Abstract][Full Text] [Related]
18. Algorithms for Diagnosis of Diabetic Retinopathy and Diabetic Macula Edema- A Review.
Suriyasekeran K; Santhanamahalingam S; Duraisamy M
Adv Exp Med Biol; 2021; 1307():357-373. PubMed ID: 32166636
[TBL] [Abstract][Full Text] [Related]
19. Introducing a Novel Layer in Convolutional Neural Network for Automatic Identification of Diabetic Retinopathy.
Khojasteh P; Aliahmad B; Arjunan SP; Kumar DK
Annu Int Conf IEEE Eng Med Biol Soc; 2018 Jul; 2018():5938-5941. PubMed ID: 30441688
[TBL] [Abstract][Full Text] [Related]
20. Joint segmentation and classification of retinal arteries/veins from fundus images.
Girard F; Kavalec C; Cheriet F
Artif Intell Med; 2019 Mar; 94():96-109. PubMed ID: 30871687
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
