Keratoconus Detection-based on Dynamic Corneal Deformation Videos Using Deep Learning

[1]  Siamak Yousefi,et al.  Detecting dry eye from ocular surface videos based on deep learning. , 2023, The ocular surface.

[2]  V. Jhanji,et al.  Artificial Intelligence–Based Diagnostic Model for Detecting Keratoconus Using Videos of Corneal Force Deformation , 2022, Translational vision science & technology.

[3]  Zaid Abdi Alkareem Alyasseri,et al.  A Hybrid Deep Learning Construct for Detecting Keratoconus From Corneal Maps , 2021, Translational vision science & technology.

[4]  L. Tian,et al.  Comparative analysis of the morphological and biomechanical properties of normal cornea and keratoconus at different stages , 2021, International Ophthalmology.

[5]  F. Raiskup,et al.  Development of a classification system based on corneal biomechanical properties using artificial intelligence predicting keratoconus severity , 2021, Eye and Vision.

[6]  Shital S. Chiddarwar,et al.  A Review of Video Generation Approaches , 2020, 2020 International Conference on Power, Instrumentation, Control and Computing (PICC).

[7]  Hazem Abdelmotaal,et al.  Classification of Color-Coded Scheimpflug Camera Corneal Tomography Images Using Deep Learning , 2020, Translational vision science & technology.

[8]  Shandong Wu,et al.  Handling imbalanced medical image data: A deep-learning-based one-class classification approach , 2020, Artif. Intell. Medicine.

[9]  Geoffrey I. Webb.  Naïve Bayes , 2020, Encyclopedia of Machine Learning.

[10]  R. Bahadur,et al.  Keratoconus , 2020, All about Your Eyes, Second Edition, revised and updated.

[11]  K. Nishida,et al.  Correlation Between Corneal Biomechanical Indices and the Severity of Keratoconus. , 2020, Cornea.

[12]  Taghi M. Khoshgoftaar,et al.  A survey on Image Data Augmentation for Deep Learning , 2019, Journal of Big Data.

[13]  F. Raiskup,et al.  Assessment of corneal biomechanical parameters in healthy and keratoconic eyes using dynamic bidirectional applanation device and dynamic Scheimpflug analyzer. , 2019, Journal of cataract and refractive surgery.

[14]  R. Ambrósio,et al.  Accuracy of Scheimpflug-derived corneal biomechanical and tomographic indices for detecting subclinical and mild keratectasia in a South Asian population. , 2019, Journal of cataract and refractive surgery.

[15]  W. Price,et al.  Privacy in the age of medical big data , 2019, Nature Medicine.

[16]  Marcella Q. Salomão,et al.  Enhanced Ectasia Detection Using Corneal Tomography and Biomechanics. , 2019, American journal of ophthalmology.

[17]  Xiaodong Wang,et al.  An Improved DenseNet Method Based on Transfer Learning for Fundus Medical Images , 2018, 2018 7th International Conference on Digital Home (ICDH).

[18]  R. Koprowski,et al.  Corneal Vibrations during Intraocular Pressure Measurement with an Air-Puff Method , 2018, Journal of healthcare engineering.

[19]  Marcella Q. Salomão,et al.  Integration of Scheimpflug-Based Corneal Tomography and Biomechanical Assessments for Enhancing Ectasia Detection. , 2017, Journal of refractive surgery.

[20]  Renato Ambrósio,et al.  Biomechanical Characterization of Subclinical Keratoconus Without Topographic or Tomographic Abnormalities. , 2017, Journal of refractive surgery.

[21]  A. Elsheikh,et al.  Introduction of Two Novel Stiffness Parameters and Interpretation of Air Puff-Induced Biomechanical Deformation Parameters With a Dynamic Scheimpflug Analyzer. , 2017, Journal of refractive surgery.

[22]  Bernardo T. Lopes,et al.  Detection of Keratoconus With a New Biomechanical Index. , 2016, Journal of refractive surgery.

[23]  Sebastian Ruder,et al.  An overview of gradient descent optimization algorithms , 2016, Vestnik komp'iuternykh i informatsionnykh tekhnologii.

[24]  Bernardo T. Lopes,et al.  Influence of Pachymetry and Intraocular Pressure on Dynamic Corneal Response Parameters in Healthy Patients. , 2016, Journal of refractive surgery.

[25]  Bolei Zhou,et al.  Learning Deep Features for Discriminative Localization , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[26]  Xu Sun,et al.  Fast Implementation of DeLong’s Algorithm for Comparing the Areas Under Correlated Receiver Operating Characteristic Curves , 2014, IEEE Signal Processing Letters.

[27]  D. Patel,et al.  Biomechanical responses of healthy and keratoconic corneas measured using a noncontact scheimpflug-based tonometer. , 2014, Investigative ophthalmology & visual science.

[28]  William J Dupps,et al.  Biomechanics of corneal ectasia and biomechanical treatments. , 2014, Journal of cataract and refractive surgery.

[29]  G. Labiris,et al.  Impact of Keratoconus, Cross-Linking and Cross-Linking Combined With Photorefractive Keratectomy on Self-Reported Quality of Life , 2012, Cornea.

[30]  Wei Chen,et al.  Improved Zhang-Suen thinning algorithm in binary line drawing applications , 2012, 2012 International Conference on Systems and Informatics (ICSAI2012).

[31]  David M. W. Powers,et al.  Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation , 2011, ArXiv.

[32]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[33]  Renato Ambrósio,et al.  Imaging of the cornea: topography vs tomography. , 2010, Journal of refractive surgery.

[34]  K. Zadnik,et al.  Collaborative Longitudinal Evaluation of Keratoconus (CLEK) Study: methods and findings to date. , 2007, Contact lens & anterior eye : the journal of the British Contact Lens Association.

[35]  Travis E. Oliphant,et al.  Python for Scientific Computing , 2007, Computing in Science & Engineering.

[36]  Kevin Anderson,et al.  Application of structural analysis to the mechanical behaviour of the cornea , 2004, Journal of The Royal Society Interface.

[37]  Ana Maria Mendonça,et al.  Data Augmentation for Improving Proliferative Diabetic Retinopathy Detection in Eye Fundus Images , 2020, IEEE Access.

[38]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..