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For: Gómez-Valverde JJ, Antón A, Fatti G, Liefers B, Herranz A, Santos A, Sánchez CI, Ledesma-Carbayo MJ. Automatic glaucoma classification using color fundus images based on convolutional neural networks and transfer learning. Biomed Opt Express 2019;10:892-913. [PMID: 30800522 DOI: 10.1364/BOE.10.000892] [Cited by in Crossref: 48] [Cited by in F6Publishing: 7] [Article Influence: 16.0] [Reference Citation Analysis]
Number Citing Articles
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11 Le D, Alam M, Yao CK, Lim JI, Hsieh YT, Chan RVP, Toslak D, Yao X. Transfer Learning for Automated OCTA Detection of Diabetic Retinopathy. Transl Vis Sci Technol 2020;9:35. [PMID: 32855839 DOI: 10.1167/tvst.9.2.35] [Cited by in Crossref: 8] [Cited by in F6Publishing: 4] [Article Influence: 4.0] [Reference Citation Analysis]
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13 Claro M, Veras R, Santana A, Araújo F, Silva R, Almeida J, Leite D. An hybrid feature space from texture information and transfer learning for glaucoma classification. Journal of Visual Communication and Image Representation 2019;64:102597. [DOI: 10.1016/j.jvcir.2019.102597] [Cited by in Crossref: 14] [Cited by in F6Publishing: 5] [Article Influence: 4.7] [Reference Citation Analysis]
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16 Anton A, Nolivos K, Pazos M, Fatti G, Herranz A, Ayala-Fuentes ME, Martínez-Prats E, Peral O, Vega-Lopez Z, Monleon-Getino A, Morilla-Grasa A, Comas M, Castells X. Interobserver and Intertest Agreement in Telemedicine Glaucoma Screening with Optic Disk Photos and Optical Coherence Tomography. J Clin Med 2021;10:3337. [PMID: 34362120 DOI: 10.3390/jcm10153337] [Reference Citation Analysis]
17 Buddhavarapu VG, J AAJ. An experimental study on classification of thyroid histopathology images using transfer learning. Pattern Recognition Letters 2020;140:1-9. [DOI: 10.1016/j.patrec.2020.09.020] [Cited by in Crossref: 4] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
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20 Aggarwal R, Sounderajah V, Martin G, Ting DSW, Karthikesalingam A, King D, Ashrafian H, Darzi A. Diagnostic accuracy of deep learning in medical imaging: a systematic review and meta-analysis. NPJ Digit Med 2021;4:65. [PMID: 33828217 DOI: 10.1038/s41746-021-00438-z] [Cited by in Crossref: 6] [Cited by in F6Publishing: 8] [Article Influence: 6.0] [Reference Citation Analysis]
21 Hood DC, La Bruna S, Tsamis E, Thakoor KA, Rai A, Leshno A, de Moraes CGV, Cioffi GA, Liebmann JM. Detecting glaucoma with only OCT: Implications for the clinic, research, screening, and AI development. Prog Retin Eye Res 2022;:101052. [PMID: 35216894 DOI: 10.1016/j.preteyeres.2022.101052] [Reference Citation Analysis]
22 Li F, Yan L, Wang Y, Shi J, Chen H, Zhang X, Jiang M, Wu Z, Zhou K. Deep learning-based automated detection of glaucomatous optic neuropathy on color fundus photographs. Graefes Arch Clin Exp Ophthalmol 2020;258:851-67. [PMID: 31989285 DOI: 10.1007/s00417-020-04609-8] [Cited by in Crossref: 12] [Cited by in F6Publishing: 5] [Article Influence: 6.0] [Reference Citation Analysis]
23 Santosh NK, Barpanda SS. Wavelet and PCA-based glaucoma classification through novel methodological enhanced retinal images. Machine Vision and Applications 2022;33. [DOI: 10.1007/s00138-021-01263-w] [Reference Citation Analysis]
24 Ibrahim MH, Hacibeyoglu M, Agaoglu A, Ucar F. Glaucoma disease diagnosis with an artificial algae-based deep learning algorithm. Med Biol Eng Comput 2022. [PMID: 35080695 DOI: 10.1007/s11517-022-02510-6] [Reference Citation Analysis]
25 Camara J, Neto A, Pires IM, Villasana MV, Zdravevski E, Cunha A. Literature Review on Artificial Intelligence Methods for Glaucoma Screening, Segmentation, and Classification. J Imaging 2022;8:19. [DOI: 10.3390/jimaging8020019] [Reference Citation Analysis]
26 Ishii K, Asaoka R, Omoto T, Mitaki S, Fujino Y, Murata H, Onoda K, Nagai A, Yamaguchi S, Obana A, Tanito M. Predicting intraocular pressure using systemic variables or fundus photography with deep learning in a health examination cohort. Sci Rep 2021;11:3687. [PMID: 33574359 DOI: 10.1038/s41598-020-80839-4] [Reference Citation Analysis]
27 Noma S, Yamashita T, Asaoka R, Terasaki H, Yoshihara N, Kakiuchi N, Sakamoto T. Sex judgment using color fundus parameters in elementary school students. Graefes Arch Clin Exp Ophthalmol 2020;258:2781-9. [PMID: 33064194 DOI: 10.1007/s00417-020-04969-1] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
28 Suguna G, Lavanya R. Performance Assessment of EyeNet Model in Glaucoma Diagnosis. Pattern Recognit Image Anal 2021;31:334-44. [DOI: 10.1134/s1054661821020164] [Reference Citation Analysis]
29 Ganesh SS, Kannayeram G, Karthick A, Muhibbullah M. A Novel Context Aware Joint Segmentation and Classification Framework for Glaucoma Detection. Comput Math Methods Med 2021;2021:2921737. [PMID: 34777561 DOI: 10.1155/2021/2921737] [Reference Citation Analysis]
30 Neto A, Camara J, Cunha A. Evaluations of Deep Learning Approaches for Glaucoma Screening Using Retinal Images from Mobile Device. Sensors 2022;22:1449. [DOI: 10.3390/s22041449] [Reference Citation Analysis]