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Journal Abstract Search

174 related items for PubMed ID: 36370579

  • 1. Recent trends and advances in fundus image analysis: A review.
    Iqbal S, Khan TM, Naveed K, Naqvi SS, Nawaz SJ.
    Comput Biol Med; 2022 Dec; 151(Pt A):106277. PubMed ID: 36370579
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  • 3. Retinal image analysis for disease screening through local tetra patterns.
    Porwal P, Pachade S, Kokare M, Giancardo L, Mériaudeau F.
    Comput Biol Med; 2018 Nov 01; 102():200-210. PubMed ID: 30308336
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  • 4. Retinal Disease Screening Through Local Binary Patterns.
    Morales S, Engan K, Naranjo V, Colomer A.
    IEEE J Biomed Health Inform; 2017 Jan 01; 21(1):184-192. PubMed ID: 26469792
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  • 6. Diagnosis of retinal health in digital fundus images using continuous wavelet transform (CWT) and entropies.
    Koh JEW, Acharya UR, Hagiwara Y, Raghavendra U, Tan JH, Sree SV, Bhandary SV, Rao AK, Sivaprasad S, Chua KC, Laude A, Tong L.
    Comput Biol Med; 2017 May 01; 84():89-97. PubMed ID: 28351716
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  • 7. Detection of retinopathy disease using morphological gradient and segmentation approaches in fundus images.
    Toğaçar M.
    Comput Methods Programs Biomed; 2022 Feb 01; 214():106579. PubMed ID: 34896689
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  • 9. Computer-aided diagnosis based on enhancement of degraded fundus photographs.
    Jin K, Zhou M, Wang S, Lou L, Xu Y, Ye J, Qian D.
    Acta Ophthalmol; 2018 May 01; 96(3):e320-e326. PubMed ID: 29090844
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  • 11. Retinal images benchmark for the detection of diabetic retinopathy and clinically significant macular edema (CSME).
    Noor-Ul-Huda M, Tehsin S, Ahmed S, Niazi FAK, Murtaza Z.
    Biomed Tech (Berl); 2019 May 27; 64(3):297-307. PubMed ID: 30055096
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  • 17. 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 27; 99():101701. PubMed ID: 31606116
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  • 18. Construction of benchmark retinal image database for diabetic retinopathy analysis.
    Kaur J, Mittal D.
    Proc Inst Mech Eng H; 2020 Sep 27; 234(9):1036-1048. PubMed ID: 32605477
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  • 19. An Automated System for the Detection and Classification of Retinal Changes Due to Red Lesions in Longitudinal Fundus Images.
    Adal KM, van Etten PG, Martinez JP, Rouwen KW, Vermeer KA, van Vliet LJ.
    IEEE Trans Biomed Eng; 2018 Jun 27; 65(6):1382-1390. PubMed ID: 28922110
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  • 20. Ensemble of deep convolutional neural networks is more accurate and reliable than board-certified ophthalmologists at detecting multiple diseases in retinal fundus photographs.
    Pandey PU, Ballios BG, Christakis PG, Kaplan AJ, Mathew DJ, Ong Tone S, Wan MJ, Micieli JA, Wong JCY.
    Br J Ophthalmol; 2024 Feb 21; 108(3):417-423. PubMed ID: 36720585
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