TY - JOUR
T1 - Retinal Diseases Classification Using Levenberg-Marquath (LM) Learning Algorithm for Pi Sigma Network (PSN) and Principal Component Analysis (PCA) Methods
AU - Swasono, S.
AU - Damayanti, A.
AU - Pratiwi, A. B.
N1 - Publisher Copyright:
© 2019 IOP Publishing Ltd. All rights reserved.
PY - 2019/9/9
Y1 - 2019/9/9
N2 - Eye is one of the most important part of the body that has many parts or layers, one of which is the retina of the eye. Retina is a slight layer coating the back of the eye and serves to receive light, converting light into nerve signals which then sent to the brain. As well as the other body parts, retina is also able of experiencing disorders or abnormalities that can inhibit the vision process. Several of the retinal eye diseases are Retinal Ablatio, Age-related Macular Degeneration, and Diabetic Retinopathy. Eye fundus examination can be used to classify eye retinal diseases. Pi Sigma Network with Lavenberg Marquath Learning Algorithm is a method applied for classification of eye retinal diseases. Before executing classification process, image processing resulting grayscale image and data reduction using Principal Components Analysis are carried out for reducing the size of the fundus image. The accuracy obtained from the classification process using the applied methods is 100%.
AB - Eye is one of the most important part of the body that has many parts or layers, one of which is the retina of the eye. Retina is a slight layer coating the back of the eye and serves to receive light, converting light into nerve signals which then sent to the brain. As well as the other body parts, retina is also able of experiencing disorders or abnormalities that can inhibit the vision process. Several of the retinal eye diseases are Retinal Ablatio, Age-related Macular Degeneration, and Diabetic Retinopathy. Eye fundus examination can be used to classify eye retinal diseases. Pi Sigma Network with Lavenberg Marquath Learning Algorithm is a method applied for classification of eye retinal diseases. Before executing classification process, image processing resulting grayscale image and data reduction using Principal Components Analysis are carried out for reducing the size of the fundus image. The accuracy obtained from the classification process using the applied methods is 100%.
UR - http://www.scopus.com/inward/record.url?scp=85072676342&partnerID=8YFLogxK
U2 - 10.1088/1742-6596/1306/1/012048
DO - 10.1088/1742-6596/1306/1/012048
M3 - Conference article
AN - SCOPUS:85072676342
SN - 1742-6588
VL - 1306
JO - Journal of Physics: Conference Series
JF - Journal of Physics: Conference Series
IS - 1
M1 - 012048
T2 - 2nd International Conference on Mathematics: Education, Theory, and Application, ICMETA 2018
Y2 - 30 October 2018 through 31 October 2018
ER -