Comparison of machine learning and traditional classifiers in glaucoma diagnosis
By Kwokleung Chan, Te-Won Lee, Pamela A. Sample, Michael H. Goldbaum, Robert N. Weinreb, Terrence J. Sejnowski. 2002
215 citations recorded by the source corpus.
Subfields: Computer Vision and Pattern Recognition, Ophthalmology, Radiology, Nuclear Medicine and Imaging
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Vision and Pattern Recognition
- Tim Miller
- Marimuthu Swami Palaniswami
- Chandan Kumar Karmakar
- Hanruo Liu
- Ahsan Habib Khandoker
- Brendan Owen
- Rezaul K. Begg
Citations in the corpus
- Explanation in Artificial Intelligence: Insights from the Social Sciences (2017, background): “For example, consider the application of assessing the risk of glaucoma [22].”
- Combination of independent component analysis and support vector machines for intelligent faults diagnosis of induction motors (2007, unknown): “…reported in classifying human cytochrome P450 enzyme (Kriegl, Arnhold, Beck, & Fox, 2005), financial analysis (Min & Lee, 2005), chemical process (Guo, Xie, Wang, & Zhang, 2003), biomedical (Chan & Lee, 2002), image processing and face recognition (Antonini, Popovici, & Thiran, 2005) and so on.”
- Support Vector Machines for Automated Recognition of Obstructive Sleep Apnea Syndrome From ECG Recordings (2009, background): “Those data vectors nearest to the constructed line in the transformed space are called the support vectors (SVs).”
- Support vector machines for automated gait classification (2005, methodology): “Recently, supportvectormachines(SVMs),amachinelearningtechnique,have been shown to be a powerful tool for learning from data and for solvingclassificationandregressionproblemswithsuperiorclassification performance [10]–[14].”
- Automated Diagnosis of Glaucoma Using Digital Fundus Images (2009, background): “…support vector machine (SVM), and linear (LDA) and quadratic discriminant analysis (QDA), Parzen window, mixture of Gaussian (MOG), and mixture of generalized Gaussian (MGG) with STATPAC indexes mean deviation, pattern standard deviation, and corrected pattern standard deviation was studied [6].”
- Digital technology, tele-medicine and artificial intelligence in ophthalmology: A global perspective (2020, background): “Further studies similarly demonstrated that ML may perform comparably or better than human experts in the mean deviation, pattern standard deviation and glaucoma hemifield test (Goldbaum et al., 2002; Chan et al., 2002).”