Open AccessIdentification of Retinoblastoma Using the Extreme Learning Machine
Author(s) -
Ivan Jaya,
U Andayani,
Baihaqi Siregar,
Tio Febri,
Dedy Arisandi
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1235/1/012057
Subject(s) - retinoblastoma , fundus (uterus) , artificial intelligence , computer science , pupil , retina , adaptive histogram equalization , computer vision , thresholding , retinal , ophthalmology , image processing , medicine , histogram equalization , image (mathematics) , optics , biology , genetics , gene , physics
Retinoblastoma is cancer on the retina that often occurs in infants and children that can cause blindness and even death. The cause of retinoblastoma is due to the mutation of the RB1 gene which keeps retinal cells reproducing until the tumor grows on the retina. In general, to identify a retinoblastoma the doctor uses an ophthalmoscope that shines brightly through the pupil to examine the back of the eye and see the presence of white or yellowish white tumor lesions in the eye. In addition, the examination is also done by analyzing the retinal fundus image from the fundus camera. The image of the fundus camera is re-analyzed by a doctor or expert to determine whether or not there is retinoblastoma. Therefore, a system is needed to help the expert to diagnose retinoblastoma. The method used in this study is the extreme learning machine. Retinal fundus images are used as input images to identify retinoblastoma. Before being identified, pre-processing of the image is carried out, it consists of scaling, green channel, contrast stretching, thresholding, and feature extraction using the zoning method. From this study, it was concluded that the proposed method had the ability to identify retinoblastoma with an accuracy of 92%.