The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Untuk mengelakkan kebutaan akibat retinopati diabetik, pemeriksaan berkala dan diagnosis awal adalah perlu. Disebabkan kekurangan pakar oftalmologi di kawasan luar bandar, pengesanan eksudat awal automatik (salah satu tanda retinopati diabetes) boleh membantu mengurangkan bilangan kebutaan pada pesakit diabetes. Kaedah pengesanan eksudat automatik tradisional adalah berdasarkan konfigurasi parameter tertentu, manakala pendekatan pembelajaran mesin yang kelihatan lebih fleksibel mungkin memerlukan kos yang tinggi dari segi pengiraan. Analisis perbandingan pembelajaran tradisional dan mesin pengesanan eksudat, iaitu, morfologi matematik, pengelasan c-means kabur, pengelas Bayesian naif, Mesin Vektor Sokongan dan pengelas Nearest Neighbor dibentangkan. Eksudat yang dikesan disahkan dengan kebenaran tanah yang dilukis tangan pakar oftalmologi. Kepekaan, kekhususan, ketepatan, ketepatan dan kerumitan masa setiap kaedah juga dibandingkan.
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Salinan
Akara SOPHARAK, Bunyarit UYYANONVARA, Sarah BARMAN, Thomas WILLIAMSON, "Comparative Analysis of Automatic Exudate Detection between Machine Learning and Traditional Approaches" in IEICE TRANSACTIONS on Information,
vol. E92-D, no. 11, pp. 2264-2271, November 2009, doi: 10.1587/transinf.E92.D.2264.
Abstract: To prevent blindness from diabetic retinopathy, periodic screening and early diagnosis are neccessary. Due to lack of expert ophthalmologists in rural area, automated early exudate (one of visible sign of diabetic retinopathy) detection could help to reduce the number of blindness in diabetic patients. Traditional automatic exudate detection methods are based on specific parameter configuration, while the machine learning approaches which seems more flexible may be computationally high cost. A comparative analysis of traditional and machine learning of exudates detection, namely, mathematical morphology, fuzzy c-means clustering, naive Bayesian classifier, Support Vector Machine and Nearest Neighbor classifier are presented. Detected exudates are validated with expert ophthalmologists' hand-drawn ground-truths. The sensitivity, specificity, precision, accuracy and time complexity of each method are also compared.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E92.D.2264/_p
Salinan
@ARTICLE{e92-d_11_2264,
author={Akara SOPHARAK, Bunyarit UYYANONVARA, Sarah BARMAN, Thomas WILLIAMSON, },
journal={IEICE TRANSACTIONS on Information},
title={Comparative Analysis of Automatic Exudate Detection between Machine Learning and Traditional Approaches},
year={2009},
volume={E92-D},
number={11},
pages={2264-2271},
abstract={To prevent blindness from diabetic retinopathy, periodic screening and early diagnosis are neccessary. Due to lack of expert ophthalmologists in rural area, automated early exudate (one of visible sign of diabetic retinopathy) detection could help to reduce the number of blindness in diabetic patients. Traditional automatic exudate detection methods are based on specific parameter configuration, while the machine learning approaches which seems more flexible may be computationally high cost. A comparative analysis of traditional and machine learning of exudates detection, namely, mathematical morphology, fuzzy c-means clustering, naive Bayesian classifier, Support Vector Machine and Nearest Neighbor classifier are presented. Detected exudates are validated with expert ophthalmologists' hand-drawn ground-truths. The sensitivity, specificity, precision, accuracy and time complexity of each method are also compared.},
keywords={},
doi={10.1587/transinf.E92.D.2264},
ISSN={1745-1361},
month={November},}
Salinan
TY - JOUR
TI - Comparative Analysis of Automatic Exudate Detection between Machine Learning and Traditional Approaches
T2 - IEICE TRANSACTIONS on Information
SP - 2264
EP - 2271
AU - Akara SOPHARAK
AU - Bunyarit UYYANONVARA
AU - Sarah BARMAN
AU - Thomas WILLIAMSON
PY - 2009
DO - 10.1587/transinf.E92.D.2264
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E92-D
IS - 11
JA - IEICE TRANSACTIONS on Information
Y1 - November 2009
AB - To prevent blindness from diabetic retinopathy, periodic screening and early diagnosis are neccessary. Due to lack of expert ophthalmologists in rural area, automated early exudate (one of visible sign of diabetic retinopathy) detection could help to reduce the number of blindness in diabetic patients. Traditional automatic exudate detection methods are based on specific parameter configuration, while the machine learning approaches which seems more flexible may be computationally high cost. A comparative analysis of traditional and machine learning of exudates detection, namely, mathematical morphology, fuzzy c-means clustering, naive Bayesian classifier, Support Vector Machine and Nearest Neighbor classifier are presented. Detected exudates are validated with expert ophthalmologists' hand-drawn ground-truths. The sensitivity, specificity, precision, accuracy and time complexity of each method are also compared.
ER -