Relevant bibliographies by topics / Ophthalmic classification

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Ophthalmic classification'

Author: Grafiati
Published: 5 June 2025
Last updated: 1 August 2025

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Journal articles on the topic "Ophthalmic classification"

1

Spencer, Louis M., and Gregory R. Spencer. "A New Classification of Ophthalmic Disorders with Standardized Ophthalmic Abbreviations." Ophthalmology 97, no. 3 (1990): 385–89. http://dx.doi.org/10.1016/s0161-6420(90)32576-9.

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Liu, Fangyuan, Bo Qin, and Fengqi Jiang. "Eye Disease Net: an algorithmic model for rapid diagnosis of diseases." PeerJ Computer Science 9 (December 12, 2023): e1672. http://dx.doi.org/10.7717/peerj-cs.1672.

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With the development of science and technology and the improvement of the quality of life, ophthalmic diseases have become one of the major disorders that affect the quality of life of people. In view of this, we propose a new method of ophthalmic disease classification, ED-Net (Eye Disease Classification Net), which is composed of the ED_Resnet model and ED_Xception model, and we compare our ED-Net method with classical classification algorithms, transformer algorithm, more advanced image classification algorithms and ophthalmic disease classification algorithms. We propose the ED_Resnet modu
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Khalaf, Ahmed Tuama, and Salwa Khalid Abdulateef. "Ophthalmic Diseases Classification Based on YOLOv8." Journal of Robotics and Control (JRC) 5, no. 2 (2024): 408–15. https://doi.org/10.18196/jrc.v5i2.21208.

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With the rising prevalence of retinal diseases, identifying eye diseases at an early stage is crucial for effective treatment and prevention of irreversible blindness. But Ophthalmologists face challenges in detecting subtle symptoms that may indicate the presence of a disease before it progresses to an advanced stage Among these challenges, eye diseases can present with a wide range of symptoms, and some conditions may share similar signs. To solve these difficulties, in the research proposed YOLOV8(You Only Look Once) Lightweight Self-Attention model to classify seven different retinal disea
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Arshinoff, Steve A., and Masoud Jafari. "New classification of ophthalmic viscosurgical devices—2005." Journal of Cataract & Refractive Surgery 31, no. 11 (2005): 2167–71. http://dx.doi.org/10.1016/j.jcrs.2005.08.056.

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Gibertoni, Giovanni, Guido Borghi, and Luigi Rovati. "Vision-Based Eye Image Classification for Ophthalmic Measurement Systems." Sensors 23, no. 1 (2022): 386. http://dx.doi.org/10.3390/s23010386.

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The accuracy and the overall performances of ophthalmic instrumentation, where specific analysis of eye images is involved, can be negatively influenced by invalid or incorrect frames acquired during everyday measurements of unaware or non-collaborative human patients and non-technical operators. Therefore, in this paper, we investigate and compare the adoption of several vision-based classification algorithms belonging to different fields, i.e., Machine Learning, Deep Learning, and Expert Systems, in order to improve the performance of an ophthalmic instrument designed for the Pupillary Light
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Sever, Irina N., Anna E. Gerasyuta, Dmitry V. Litvinenko, and Vyacheslav V. Tkachev. "Classification of ophthalmic segment internal carotid artery aneurysms: a literature review." Sechenov Medical Journal 15, no. 4 (2024): 4–18. https://doi.org/10.47093/2218-7332.2024.15.4.4-18.

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Aneurysms of the ophthalmic segment of the internal carotid artery (ICA) have traditionally been considered complex, due to the peculiarities of their anatomy, difficult accessibility for open surgery and the risk of deterioration of visual functions. The high variability of the location of carotid-ophthalmic (paraclinoid) aneurysms is the main reason for the lack of a generally recognized clinical and anatomical classification of aneurysms of this localization. In turn, the lack of anatomical unification limits the communication between specialists involved in the diagnosis and treatment of s
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Hong, Jie. "Design and Application of Nursing Record Sheet and Operation Registration Form for Ophthalmic Daytime Operation." International Journal of Studies in Nursing 7, no. 1 (2022): 32. http://dx.doi.org/10.20849/ijsn.v7i1.1082.

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Objective: To explore the effect of nursing record sheet and operation registration form in daytime ophthalmic surgery. Methods: the nursing record sheet and operation registration form of daytime ophthalmic surgery were designed and used to evaluate the incidence of adverse events and the satisfaction of doctors in the operating room before and after use. Results: from January 2018 to June 2019, there were 15 adverse events caused by unclear handover, wrong record, wrong eye classification, wrong application of antibiotics and waiting for surgical consumables in ophthalmology due to nursing r
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Smaida, Mahmoud, Serhii Yaroshchak, and Ahmed Y. Ben Sasi. "Learning Rate Optimization in CNN for Accurate Ophthalmic Classification." International Journal of Innovative Technology and Exploring Engineering 10, no. 4 (2021): 211–16. http://dx.doi.org/10.35940/ijitee.b8259.0210421.

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One of the most important hyper-parameters for model training and generalization is the learning rate. Recently, many research studies have shown that optimizing the learning rate schedule is very useful for training deep neural networks to get accurate and efficient results. In this paper, different learning rate schedules using some comprehensive optimization techniques have been compared in order to measure the accuracy of a convolutional neural network CNN model to classify four ophthalmic conditions. In this work, a deep learning CNN based on Keras and TensorFlow has been deployed using P
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Melillo, Paolo, Ada Orrico, Franco Chirico, et al. "Identifying fallers among ophthalmic patients using classification tree methodology." PLOS ONE 12, no. 3 (2017): e0174083. http://dx.doi.org/10.1371/journal.pone.0174083.

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Rai, NN. "Introduction to ophthalmic cytology - modalities and classification of neoplasms." Journal of Cytology 24, no. 1 (2007): 11. http://dx.doi.org/10.4103/0970-9371.42083.

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Dissertations / Theses on the topic "Ophthalmic classification"

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Walsh, P. E. "Variable selection for classification in complex ophthalmic data : a multivariate statistical framework." Thesis, University of Liverpool, 2017. http://livrepository.liverpool.ac.uk/3019718/.

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Variable selection is an essential part of the process of model-building for classification or prediction. Some of the challenges of variable selection are heterogeneous variance-covariance matrices, differing scales of variables, non-normally distributed data and missing data. Statistical methods exist for variable selection however these are often univariate, make restrictive assumptions about the distribution of data or are expensive in terms of the computational power required. In this thesis I focus on filter methods of variable selection that are computationally fast and propose a metric
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Colomer, Granero Adrián. "Fundus image analysis for automatic screening of ophthalmic pathologies." Doctoral thesis, Universitat Politècnica de València, 2018. http://hdl.handle.net/10251/99745.

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En los ultimos años el número de casos de ceguera se ha reducido significativamente. A pesar de este hecho, la Organización Mundial de la Salud estima que un 80% de los casos de pérdida de visión (285 millones en 2010) pueden ser evitados si se diagnostican en sus estadios más tempranos y son tratados de forma efectiva. Para cumplir esta propuesta se pretende que los servicios de atención primaria incluyan un seguimiento oftalmológico de sus pacientes así como fomentar campañas de cribado en centros proclives a reunir personas de alto riesgo. Sin embargo, estas soluciones exigen una alta carga
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Díaz, Pinto Andrés Yesid. "Machine Learning for Glaucoma Assessment using Fundus Images." Doctoral thesis, Universitat Politècnica de València, 2019. http://hdl.handle.net/10251/124351.

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[ES] Las imágenes de fondo de ojo son muy utilizadas por los oftalmólogos para la evaluación de la retina y la detección de glaucoma. Esta patología es la segunda causa de ceguera en el mundo, según estudios de la Organización Mundial de la Salud (OMS). En esta tesis doctoral, se estudian algoritmos de aprendizaje automático (machine learning) para la evaluación automática del glaucoma usando imágenes de fondo de ojo. En primer lugar, se proponen dos métodos para la segmentación automática. El primer método utiliza la transformación Watershed Estocástica para segmentar la copa óptica y post
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Chiu, Stephanie Ja-Yi. "Graph Theory and Dynamic Programming Framework for Automated Segmentation of Ophthalmic Imaging Biomarkers." Diss., 2014. http://hdl.handle.net/10161/8688.

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<p>Accurate quantification of anatomical and pathological structures in the eye is crucial for the study and diagnosis of potentially blinding diseases. Earlier and faster detection of ophthalmic imaging biomarkers also leads to optimal treatment and improved vision recovery. While modern optical imaging technologies such as optical coherence tomography (OCT) and adaptive optics (AO) have facilitated in vivo visualization of the eye at the cellular scale, the massive influx of data generated by these systems is often too large to be fully analyzed by ophthalmic experts without extensive time o
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Books on the topic "Ophthalmic classification"

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Spencer, Louis M. Spencer's current ophthalmic terminology & abbreviations: Includes the classification of ophthalmic disorders, current ICD9 and CPT listings & over 2000 systematically abbreviated terms. Edited by Spencer Valerie N. MedWorld Publications, 1986.

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Club, Scheimpflug. Classification, Monitoring and Evaluation of Cataracts (Ophthalmic Research, Vol 22). S. Karger AG (Switzerland), 1990.

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Dasgupta, Bhaskar. Polymyalgia rheumatica. Oxford University Press, 2013. http://dx.doi.org/10.1093/med/9780199642489.003.0134.

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This chapter reviews advances in pathogenesis; European League Against Rheumatism/American College of Rheumatology (EULAR/ACR) classification criteria with clinical, laboratory, and ultrasound criteria for classification as polymyalgia rheumatica (PMR); the heterogeneity and overlap between PMR, inflammatory arthritis, and large-vessel vasculitis as illustrated by representative cases; recent guidelines on early and correct recognition, investigations, and management of PMR; the scope of disease-modifying agents; socio-economic impact, outcomes, and patient experience in PMR. It also discusses
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Book chapters on the topic "Ophthalmic classification"

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Hermanek, P., and L. H. Sobin. "Ophthalmic Tumours." In TNM Classification of Malignant Tumours. Springer Berlin Heidelberg, 1987. http://dx.doi.org/10.1007/978-3-642-82982-6_10.

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Damato, Bertil E., and Sarah E. Coupland. "Classification of Uveal Tumors." In Clinical Ophthalmic Oncology. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-17879-6_2.

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Damato, Bertil, and Sarah E. Coupland. "Classification of Uveal Tumors." In Clinical Ophthalmic Oncology. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-54255-8_2.

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Costin, Bryan R., Julian D. Perry, and Jill A. Foster. "Classification of Orbital Tumors." In Clinical Ophthalmic Oncology. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40492-4_2.

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Blandford, Alexander D., and Julian D. Perry. "Classification of Orbital Tumors." In Clinical Ophthalmic Oncology. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-13558-4_2.

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Damato, Bertil E., and Sarah E. Coupland. "Classification of Uveal Tumors." In Clinical Ophthalmic Oncology. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-81354-2_2.

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Bouchoucha, Y., L. Golmard, Francois Radvanyi, A. Matet, and Nathalie Cassoux. "Retinoblastoma: Molecular Classification and Stratification." In Clinical Ophthalmic Oncology. Springer Nature Switzerland, 2024. https://doi.org/10.1007/978-3-031-77177-4_8.

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Pe’er, Jacob. "Eyelid Tumors: Classification and Differential Diagnosis." In Clinical Ophthalmic Oncology. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38336-6_2.

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Pe’er, Jacob, and Shahar Frenkel. "Eyelid Tumors: Classification and Differential Diagnosis." In Clinical Ophthalmic Oncology. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-06046-6_2.

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Pe’er, Jacob, and Shahar Frenkel. "Eyelid Tumors: Classification and Differential Diagnosis." In Clinical Ophthalmic Oncology. Springer Nature Switzerland, 2024. https://doi.org/10.1007/978-3-031-72328-5_2.

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Conference papers on the topic "Ophthalmic classification"

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Venkatachalam, Chandrasekar, K. Manivannan, Priyanka Shah, Shanmugavalli Venkatachalam, and R. Sathis Kumar. "Transforming Ophthalmic Diagnosis: A Deep Learning Approach for Accurate Eye Disease Classification." In 2024 5th International Conference on Image Processing and Capsule Networks (ICIPCN). IEEE, 2024. http://dx.doi.org/10.1109/icipcn63822.2024.00061.

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Mapanao, Jay Ryan, and Paulo Luis Lozano. "Utilizing Swin Transformer for the Classification of Ophthalmic Diseases in Optical Coherence Tomography (OCT) Images: A Novel Approach." In 2024 6th International Conference on Computer Communication and the Internet (ICCCI). IEEE, 2024. http://dx.doi.org/10.1109/iccci62159.2024.10674599.

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Le, David, Minhaj Alam, Taeyoon Son, Jennifer I. Lim, and Xincheng Yao. "Deep learning artery-vein classification in OCT angiography." In Ophthalmic Technologies XXXI, edited by Daniel X. Hammer, Karen M. Joos, and Daniel V. Palanker. SPIE, 2021. http://dx.doi.org/10.1117/12.2577304.

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Toslak, Devrim, Jennifer I. Lim, Xincheng Yao, Minhaj Nur Alam, and Taeyoon Son. "Automated classification and quantitative analysis of arterial and venous vessels in fundus images." In Ophthalmic Technologies XXVIII, edited by Fabrice Manns, Per G. Söderberg, and Arthur Ho. SPIE, 2018. http://dx.doi.org/10.1117/12.2290121.

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Ebrahimi, Behrouz, David Le, Mansour Abtahi, et al. "Evaluating color efficacy in fundus photography for artificial intelligence classification of retinopathy of prematurity." In Ophthalmic Technologies XXXIV, edited by Kostadinka Bizheva, Daniel X. Hammer, and Georg Schuele. SPIE, 2024. http://dx.doi.org/10.1117/12.3003192.

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Son, Taeyoon, David Le, Jennifer I. Lim, and Xincheng Yao. "Quantitative OCT features of outer retina for early diagnosis and objective classification of diabetic retinopathy." In Ophthalmic Technologies XXXI, edited by Daniel X. Hammer, Karen M. Joos, and Daniel V. Palanker. SPIE, 2021. http://dx.doi.org/10.1117/12.2577255.

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Cheng, Yuxuan, Yingxin Cao, Qinqin Zhang, Zhongdi Chu, and Ruikang K. Wang. "Deep network for retinal disease classification based on limited clinical OCT angiography datasets (Conference Presentation)." In Ophthalmic Technologies XXVIII, edited by Fabrice Manns, Per G. Söderberg, and Arthur Ho. SPIE, 2018. http://dx.doi.org/10.1117/12.2291431.

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Eppenhof, Koen, Erik Bekkers, Tos T. J. M. Berendschot, Josien P. W. Pluim, and Bart M. ter Haar Romeny. "Retinal Artery/Vein Classification via Graph Cut Optimization." In Ophthalmic Medical Image Analysis Second International Workshop. University of Iowa, 2015. http://dx.doi.org/10.17077/omia.1035.

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Tennakoon, Ruwan, Dwarikanath Mahapatra, Pallab Roy, Suman Sedai, and Rahil Garnavi. "Image Quality Classification for DR Screening Using Convolutional Neural Networks." In Ophthalmic Medical Image Analysis Third International Workshop. University of Iowa, 2016. http://dx.doi.org/10.17077/omia.1054.

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Lemaître, Guillaume, Mojdeh Rastgoo, Joan Massich, Shrinivasan Sankar, Fabrice Mériaudeau, and Désiré Sidibé. "Classification of SD-OCT Volumes with LBP: Application to DME Detection." In Ophthalmic Medical Image Analysis Second International Workshop. University of Iowa, 2015. http://dx.doi.org/10.17077/omia.1021.

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