Relevance Vector Machine and Support Vector Machine Classifier Analysis of Scanning Laser Polarimetry Retinal Nerve Fiber Layer Measurements

Bowd, Christopher; Medeiros, Felipe A.; Zhang, Zuohua; Zangwill, Linda M.; Hao, Jiucang; Lee, Te-Won; Sejnowski, Terrence J.; Weinreb, Robert N.; Goldbaum, Michael H.

doi:10.1167/iovs.04-1122

Cited by 76 publications

(48 citation statements)

References 30 publications

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“…Studies comparing these two technologies have demonstrated that the sensitivity and specificity of various RNFL parameters using the Cirrus OCT are excellent and equivalent to the Stratus OCT (9)(10)(11)(12)(13) . Since 1990 (Goldbaum MH, et al IOVS 1990;31; ARVO Abstract 503), machine learning classifier (MLC) techniques have been applied to optical imaging and visual function measurements to improve glaucoma detection, with results suggesting that these techniques are as good as or better than currently available methods at classifying eyes as glaucomatous or healthy (14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24) . Classifiers usually employ a form of supervised learning, where the program learns from positive and ne gative training examples, representing cases where, for example, there are signs of glaucoma on data obtained by examination of the visual field (positive examples) or not (negative examples).…”

Section: Sensitivity and Specificity Of Machine Learning Classifiers mentioning

confidence: 99%

Sensitivity and specificity of machine learning classifiers for glaucoma diagnosis using Spectral Domain OCT and standard automated perimetry

Silva¹,

Vidotti²,

Cremasco³

et al. 2013

Arq. Bras. Oftalmol.

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Section: Sensitivity and Specificity Of Machine Learning Classifiers mentioning

confidence: 99%

Sensitivity and specificity of machine learning classifiers for glaucoma diagnosis using Spectral Domain OCT and standard automated perimetry

Silva¹,

Vidotti²,

Cremasco³

et al. 2013

Arq. Bras. Oftalmol.

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Section: Instrumentationmentioning

confidence: 99%

“…The NFI is calculated using a support vector machine algorithm based on several RNFL measures and assigns a number from 0 to 100 to each eye. 15 The higher the NFI, the greater the likelihood the patient has glaucoma.Confocal Scanning Laser Ophthalmoscopy-The HRT II (software version 3.0, Heidelberg Engineering, Dossenheim, Germany) was used to acquire CSLO images in the study. It uses confocal scanning laser principles to obtain a 3-dimensional topographic image of the optic nerve.…”

mentioning

confidence: 99%

Comparison of Retinal Nerve Fiber Layer and Optic Disc Imaging for Diagnosing Glaucoma in Patients Suspected of Having the Disease

et al. 2008

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Purpose-To compare retinal nerve fiber layer (RNFL) and optic disc topographic imaging for detection of optic nerve damage in patients suspected of having glaucoma. Design-Observational cohort study.Participants-A cohort of 82 patients suspected of having glaucoma based on the appearance of the optic nerve.Methods-All patients were imaged using the GDx VCC scanning laser polarimeter and HRT (software version 3.0) confocal scanning laser ophthalmoscope. All patients had normal standard automated perimetry visual fields at the time of imaging and were classified based on history of documented stereophotographic evidence of progressive glaucomatous change in the appearance of the optic nerve occurring before the imaging sessions.Main Outcome Measures-Areas under the receiver operating characteristic (ROC) curves were used to evaluate the diagnostic accuracies of GDx VCC and the HRT.Results-Forty eyes with progressive glaucomatous optic nerve change were included in the glaucoma group, and 42 eyes without any evidence of progressive damage to the optic nerve followed untreated for an average time of 8.97±3.08 years were included in the normal group. The area under the ROC curve for the best parameter from GDx VCC (nerve fiber indicator [NFI]) was significantly larger than that of the best parameter from the HRT (rim volume) (0.83 vs. 0.70; P = 0.044). The NFI parameter also had a larger ROC curve area than that of the contour line-independent parameter glaucoma probability score (0.83 vs. 0.68; P = 0.023). Assuming borderline results as normal, the Moorfields regression analysis classification had a sensitivity of 48% for specificity of 69%. For a similar specificity (70%), the parameter NFI had a significantly larger sensitivity (83%) (P = 0.003).Conclusions-Retinal nerve fiber layer imaging with GDx VCC had a superior performance versus topographic optic disc assessment with the HRT for detecting early damage in patients suspected of having glaucoma. For glaucoma diagnosis, these results suggest that GDx VCC may offer advantage over the HRT when these tests are combined with clinical examination of the optic nerve.To diagnose disease, a clinician integrates the constellation of symptoms and/or signs of a presenting patient and then assigns a level of certainty regarding its presence. In the case of glaucoma evaluation, the process generally starts with the medical interview and history. It is NIH-PA Author ManuscriptNIH-PA Author Manuscript NIH-PA Author Manuscript followed by clinical examination, which generally includes slit-lamp examination, intraocular pressure (IOP) measurement, and optic nerve examination. After this information is collected, the clinician hypothesizes about the chance that glaucoma is present and can order additional tests, such as the visual field (VF).It is not unusual for a patient to present with suspicious appearance of the optic disc and normal or inconclusive VF tests. In this situation, additional testing, such as optic disc and/or retinal nerve fiber layer (RNFL) imaging,...

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“…With the advantage of handling large feature space without overfitting, SVM [36] and relevance vector machine (RVM) pioneered by Tipping [29] have shown state-of-art results in classification problems [6,29,36]. RVM is a Bayesian treatment of the sparse learning problem.…”

Section: Related Workmentioning

confidence: 99%

SBMDS: an interpretable string based malware detection system using SVM ensemble with bagging

Chen

Wang

et al. 2008

J Comput Virol

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Malicious executables are programs designed to infiltrate or damage a computer system without the owner's consent, which have become a serious threat to the security of computer systems. There is an urgent need for effective techniques to detect polymorphic, metamorphic and previously unseen malicious executables of which detection fails in most of the commercial anti-virus software. In this paper, we develop interpretable string based malware detection system (SBMDS), which is based on interpretable string analysis and uses support vector machine (SVM) ensemble with Bagging to classify the file samples and predict the exact types of the malware. programming interface (API) execution calls and important semantic strings reflecting an attacker's intent and goal. Our SBMDS is carried out with four major steps: (1) first constructing the interpretable strings by developing a feature parser; (2) performing feature selection to select informative strings related to different types of malware; (3) followed by using SVM ensemble with bagging to construct the classifier; (4) and finally conducting the malware detector, which not only can detect whether a program is malicious or not, but also can predict the exact type of the malware. Our case study on the large collection of file samples collected by Kingsoft Anti-virus lab illustrate that: (1) The accuracy and efficiency of our SBMDS outperform several popular anti-virus software; (2) Based on the signatures of interpretable strings, our SBMDS outperforms data mining based detection systems which employ single SVM, Naive Bayes with bagging, Decision Trees with bagging; (3) Compared with the IMDS which utilizes the objective-oriented association (OOA) based classification on API calls, our SBMDS achieves better performance. Our SBMDS system has already been incorporated into the scanning tool of a commercial anti-virus software.

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Relevance Vector Machine and Support Vector Machine Classifier Analysis of Scanning Laser Polarimetry Retinal Nerve Fiber Layer Measurements

Cited by 76 publications

References 30 publications

Sensitivity and specificity of machine learning classifiers for glaucoma diagnosis using Spectral Domain OCT and standard automated perimetry

Sensitivity and specificity of machine learning classifiers for glaucoma diagnosis using Spectral Domain OCT and standard automated perimetry

Comparison of Retinal Nerve Fiber Layer and Optic Disc Imaging for Diagnosing Glaucoma in Patients Suspected of Having the Disease

SBMDS: an interpretable string based malware detection system using SVM ensemble with bagging

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