scholarly journals Heidelberg Retina Tomograph Measurements of the Optic Disc and Parapapillary Retina for Detecting Glaucoma Analyzed by Machine Learning Classifiers

2004 ◽  
Vol 45 (9) ◽  
pp. 3144 ◽  
Author(s):  
Linda M. Zangwill ◽  
Kwokleung Chan ◽  
Christopher Bowd ◽  
Jicuang Hao ◽  
Te-Won Lee ◽  
...  
Keyword(s):  
Machine Learning ◽  
Optic Disc ◽  
Heidelberg Retina Tomograph ◽  
Machine Learning Classifiers ◽  
Learning Classifiers

scholarly journals Full Accuracy of Machine Learning for Differentiation between Optic Neuropathies and Pseudopapilledema

2019 ◽  
Author(s):  
Jin Mo Ahn ◽  
Sangsoo Kim ◽  
Kwang-Sung Ahn ◽  
Sung-Hoon Cho ◽  
Ungsoo Kim
Keyword(s):  
Machine Learning ◽  
Visual Acuity ◽  
Optic Disc ◽  
Optic Neuropathy ◽  
Characteristic Curve ◽  
Machine Learning Techniques ◽  
Fundus Photography ◽  
Ischemic Optic Neuropathy ◽  
Machine Learning Classifiers ◽  
Learning Classifiers

Abstract Background: This study is to evaluate the accuracy of machine learning for differentiation between optic neuropathies and pseudopapilledema (PPE). Methods: Two hundred and ninety-five images of optic neuropathies, 295 images of PPE, and 779 control images were used. Pseudopapilledema was defined as follows: cases with elevated optic nerve head and blurred disc margin, with normal visual acuity (>0.8 Snellen visual acuity), visual field, color vision, and pupillary reflex. The optic neuropathy group included cases of ischemic optic neuropathy (177), optic neuritis (48), diabetic optic neuropathy (17), papilledema (22), and retinal disorders (31). We compared four machine learning classifiers (our model, GoogleNet Inception v3, 19-layer Very Deep Convolution Network from Visual Geometry group (VGG), and 50-layer Deep Residual Learning (ResNet)). Accuracy and area under receiver operating characteristic curve (AUROC) were analyzed Results: The accuracy of machine learning classifiers ranged from 95.89% to 98.63% (our model: 95.89%, Inception V3: 96.45%, ResNet: 98.63%, and VGG: 96.80%). A high AUROC score was noted in both ResNet and VGG (0.999). Conclusions: Machine learning techniques can be combined with fundus photography as an effective approach to distinguish between PPE and elevated optic disc associated with optic neuropathies. Keywords: Machine Learning; Pseudopapilledema; Optic neuropathy; Optic disc swelling.

scholarly journals Heidelberg Retina Tomograph 3 machine learning classifiers for glaucoma detection

2008 ◽  
Vol 92 (6) ◽  
pp. 814-818 ◽  
Author(s):  
K A Townsend ◽  
G Wollstein ◽  
D Danks ◽  
K R Sung ◽  
H Ishikawa ◽  
...  
Keyword(s):  
Machine Learning ◽  
Heidelberg Retina Tomograph ◽  
Machine Learning Classifiers ◽  
Learning Classifiers ◽  
Glaucoma Detection

scholarly journals Assessing the Effect of Training Sampling Design on the Performance of Machine Learning Classifiers for Land Cover Mapping Using Multi-Temporal Remote Sensing Data and Google Earth Engine

2021 ◽  
Vol 13 (8) ◽  
pp. 1433
Author(s):  
Shobitha Shetty ◽  
Prasun Kumar Gupta ◽  
Mariana Belgiu ◽  
S. K. Srivastav
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Random Sampling ◽  
Sampling Design ◽  
Remote Sensing Data ◽  
Google Earth ◽  
Machine Learning Classifiers ◽  
Learning Classifiers ◽  
Multi Temporal ◽  
Google Earth Engine

Machine learning classifiers are being increasingly used nowadays for Land Use and Land Cover (LULC) mapping from remote sensing images. However, arriving at the right choice of classifier requires understanding the main factors influencing their performance. The present study investigated firstly the effect of training sampling design on the classification results obtained by Random Forest (RF) classifier and, secondly, it compared its performance with other machine learning classifiers for LULC mapping using multi-temporal satellite remote sensing data and the Google Earth Engine (GEE) platform. We evaluated the impact of three sampling methods, namely Stratified Equal Random Sampling (SRS(Eq)), Stratified Proportional Random Sampling (SRS(Prop)), and Stratified Systematic Sampling (SSS) upon the classification results obtained by the RF trained LULC model. Our results showed that the SRS(Prop) method favors major classes while achieving good overall accuracy. The SRS(Eq) method provides good class-level accuracies, even for minority classes, whereas the SSS method performs well for areas with large intra-class variability. Toward evaluating the performance of machine learning classifiers, RF outperformed Classification and Regression Trees (CART), Support Vector Machine (SVM), and Relevance Vector Machine (RVM) with a >95% confidence level. The performance of CART and SVM classifiers were found to be similar. RVM achieved good classification results with a limited number of training samples.

scholarly journals A review of infant cry analysis and classification

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Chunyan Ji ◽  
Thosini Bamunu Mudiyanselage ◽  
Yutong Gao ◽  
Yi Pan
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Signal Analysis ◽  
Future Research ◽  
Prosodic Features ◽  
Infant Cry ◽  
Machine Learning Classification ◽  
Machine Learning Classifiers ◽  
Learning Classifiers ◽  
Processing Techniques

AbstractThis paper reviews recent research works in infant cry signal analysis and classification tasks. A broad range of literatures are reviewed mainly from the aspects of data acquisition, cross domain signal processing techniques, and machine learning classification methods. We introduce pre-processing approaches and describe a diversity of features such as MFCC, spectrogram, and fundamental frequency, etc. Both acoustic features and prosodic features extracted from different domains can discriminate frame-based signals from one another and can be used to train machine learning classifiers. Together with traditional machine learning classifiers such as KNN, SVM, and GMM, newly developed neural network architectures such as CNN and RNN are applied in infant cry research. We present some significant experimental results on pathological cry identification, cry reason classification, and cry sound detection with some typical databases. This survey systematically studies the previous research in all relevant areas of infant cry and provides an insight on the current cutting-edge works in infant cry signal analysis and classification. We also propose future research directions in data processing, feature extraction, and neural network classification fields to better understand, interpret, and process infant cry signals.

Sign in / Sign up

Export Citation Format

Share Document