176 related articles for article (PubMed ID: 38699206)
1. A systematic review on diabetic retinopathy detection and classification based on deep learning techniques using fundus images.
Bhulakshmi D; Rajput DS
PeerJ Comput Sci; 2024; 10():e1947. PubMed ID: 38699206
[TBL] [Abstract][Full Text] [Related]
2. Attention-based deep learning framework for automatic fundus image processing to aid in diabetic retinopathy grading.
Romero-Oraá R; Herrero-Tudela M; López MI; Hornero R; García M
Comput Methods Programs Biomed; 2024 Jun; 249():108160. PubMed ID: 38583290
[TBL] [Abstract][Full Text] [Related]
3. Diabetic retinopathy screening through artificial intelligence algorithms: A systematic review.
Farahat Z; Zrira N; Souissi N; Bennani Y; Bencherif S; Benamar S; Belmekki M; Ngote MN; Megdiche K
Surv Ophthalmol; 2024 Jun; ():. PubMed ID: 38885761
[TBL] [Abstract][Full Text] [Related]
4. Diabetic Retinopathy Detection from Fundus Images of the Eye Using Hybrid Deep Learning Features.
Butt MM; Iskandar DNFA; Abdelhamid SE; Latif G; Alghazo R
Diagnostics (Basel); 2022 Jul; 12(7):. PubMed ID: 35885512
[TBL] [Abstract][Full Text] [Related]
5. Combining transfer learning with retinal lesion features for accurate detection of diabetic retinopathy.
Hassan D; Gill HM; Happe M; Bhatwadekar AD; Hajrasouliha AR; Janga SC
Front Med (Lausanne); 2022; 9():1050436. PubMed ID: 36425113
[TBL] [Abstract][Full Text] [Related]
6. A Survey on Deep-Learning-Based Diabetic Retinopathy Classification.
Sebastian A; Elharrouss O; Al-Maadeed S; Almaadeed N
Diagnostics (Basel); 2023 Jan; 13(3):. PubMed ID: 36766451
[TBL] [Abstract][Full Text] [Related]
7. 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]
8. Untangling Computer-Aided Diagnostic System for Screening Diabetic Retinopathy Based on Deep Learning Techniques.
Farooq MS; Arooj A; Alroobaea R; Baqasah AM; Jabarulla MY; Singh D; Sardar R
Sensors (Basel); 2022 Feb; 22(5):. PubMed ID: 35270949
[TBL] [Abstract][Full Text] [Related]
9. Survey on recent developments in automatic detection of diabetic retinopathy.
Bilal A; Sun G; Mazhar S
J Fr Ophtalmol; 2021 Mar; 44(3):420-440. PubMed ID: 33526268
[TBL] [Abstract][Full Text] [Related]
10. CoT-XNet: contextual transformer with Xception network for diabetic retinopathy grading.
Zhao S; Wu Y; Tong M; Yao Y; Qian W; Qi S
Phys Med Biol; 2022 Dec; 67(24):. PubMed ID: 36322995
[No Abstract] [Full Text] [Related]
11. Detection of Diabetic Retinopathy using Convolutional Neural Networks for Feature Extraction and Classification (DRFEC).
Das D; Biswas SK; Bandyopadhyay S
Multimed Tools Appl; 2022 Nov; ():1-59. PubMed ID: 36467440
[TBL] [Abstract][Full Text] [Related]
12. Deepfakes in Ophthalmology: Applications and Realism of Synthetic Retinal Images from Generative Adversarial Networks.
Chen JS; Coyner AS; Chan RVP; Hartnett ME; Moshfeghi DM; Owen LA; Kalpathy-Cramer J; Chiang MF; Campbell JP
Ophthalmol Sci; 2021 Dec; 1(4):100079. PubMed ID: 36246951
[TBL] [Abstract][Full Text] [Related]
13. Validation of Deep Convolutional Neural Network-based algorithm for detection of diabetic retinopathy - Artificial intelligence versus clinician for screening.
Shah P; Mishra DK; Shanmugam MP; Doshi B; Jayaraj H; Ramanjulu R
Indian J Ophthalmol; 2020 Feb; 68(2):398-405. PubMed ID: 31957737
[TBL] [Abstract][Full Text] [Related]
14. Synthetic Medical Images for Robust, Privacy-Preserving Training of Artificial Intelligence: Application to Retinopathy of Prematurity Diagnosis.
Coyner AS; Chen JS; Chang K; Singh P; Ostmo S; Chan RVP; Chiang MF; Kalpathy-Cramer J; Campbell JP;
Ophthalmol Sci; 2022 Jun; 2(2):100126. PubMed ID: 36249693
[TBL] [Abstract][Full Text] [Related]
15. Automatic Grading System for Diabetic Retinopathy Diagnosis Using Deep Learning Artificial Intelligence Software.
Wang XN; Dai L; Li ST; Kong HY; Sheng B; Wu Q
Curr Eye Res; 2020 Dec; 45(12):1550-1555. PubMed ID: 32410471
[No Abstract] [Full Text] [Related]
16. Deep learning for diabetic retinopathy detection and classification based on fundus images: A review.
Tsiknakis N; Theodoropoulos D; Manikis G; Ktistakis E; Boutsora O; Berto A; Scarpa F; Scarpa A; Fotiadis DI; Marias K
Comput Biol Med; 2021 Aug; 135():104599. PubMed ID: 34247130
[TBL] [Abstract][Full Text] [Related]
17. Explainable end-to-end deep learning for diabetic retinopathy detection across multiple datasets.
Chetoui M; Akhloufi MA
J Med Imaging (Bellingham); 2020 Jul; 7(4):044503. PubMed ID: 32904519
[No Abstract] [Full Text] [Related]
18. Cancer Diagnosis Using Deep Learning: A Bibliographic Review.
Munir K; Elahi H; Ayub A; Frezza F; Rizzi A
Cancers (Basel); 2019 Aug; 11(9):. PubMed ID: 31450799
[TBL] [Abstract][Full Text] [Related]
19. Synthetic artificial intelligence using generative adversarial network for retinal imaging in detection of age-related macular degeneration.
Wang Z; Lim G; Ng WY; Tan TE; Lim J; Lim SH; Foo V; Lim J; Sinisterra LG; Zheng F; Liu N; Tan GSW; Cheng CY; Cheung GCM; Wong TY; Ting DSW
Front Med (Lausanne); 2023; 10():1184892. PubMed ID: 37425325
[TBL] [Abstract][Full Text] [Related]
20. Deep learning based computer-aided diagnosis systems for diabetic retinopathy: A survey.
Asiri N; Hussain M; Al Adel F; Alzaidi N
Artif Intell Med; 2019 Aug; 99():101701. PubMed ID: 31606116
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]