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Jurnal Ilmiah FIFO Pub Date : 2020-11-01 DOI:10.22441/FIFO.2020.V12I2.007

Alva Rischa Qhisthana Pratika, Rita Magdalena, R. N. Fuadah

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引用次数: 0

摘要

青光眼是一种眼压升高导致视神经损伤的眼部疾病，是仅次于白内障的第二大致盲原因。神经损伤通常在没有症状的情况下发生，因此早期检查可以降低青光眼的风险。因此，作者设计了一种基于眼底图像的青光眼检测系统，通过形态学操作和阈值分割视盘(OD)和视杯(OC)，提取环盘比、杯盘比(CDR)、垂直杯盘比(VCDR)、水平杯盘比(HCDR)、水平垂直CDR (H-V CDR)等多种特征，方便青光眼的检测。应用人工神经网络(ANN)对青光眼进行分类。通过这种方法，可以将测试数据分为正常眼和青光眼两类。训练62条数据，测试62条数据。所得结果有助于青光眼的早期发现。对训练数据的准确率达到100%，本研究的准确率达到93.5484%。关键词:青光眼，形态学运算，阈值分割，人工神经网络青光眼:青光眼:青光眼:青光眼，青光眼。【关键词】青光眼，眼缘盘比，杯盘比(CDR)，垂直杯盘比(VCDR)，水平杯盘比(HCDR)，水平杯盘比(H-V CDR)，水平杯盘比(H-V CDR)，登干梦节段，视盘(OD)，视杯(OC)，登干梦节段形态学操作，阈值法。人工神经网络(ANN)治疗渐变色型青光眼。青光眼是指青光眼、眼青光眼、眼青光眼和眼青光眼。数据发布阳akan diambil sebanyak 62 buah数据发布阳akan diambil sebanyak 62 buah。青光眼是一种常见的青光眼。Akurasi pada data latih mencapai 100%， Akurasi pada data uji mencapai 93,5484%。青光眼，形态学运算，阈值分割，人工神经网络

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Klasifikasi Glaukoma Menggunakan Artificial Neural Network

Abstract Glaucoma is an eye disease caused by increased eyeball pressure resulting in damage to the optic nerve and the second leading cause of blindness after cataracts. Nerve damage often occurs without symptoms so that an early examination can reduce the risk of glaucoma. Therefore, the authors designed a glaucoma detection system through eye fundal images that can facilitate the detection of glaucomaby extracting various features like Rim to Disc Ratio, Cup to Disc Ratio (CDR), Vertical Cup to Disc Ratio (VCDR), Horizontal Cup to Disc Ratio (HCDR), and Horizontal to Vertical CDR (H-V CDR) with Morphological Operations dan Thresholding for segmentation of Optic Disc (OD) and Optic Cup (OC). Artificial Neural Network (ANN) is used as a classifier of glaucoma. Through this method, the test data can be divided into two classifications namely normal eyes and glaucoma eyes. 62 pieces of data will be trained and 62 pieces of data will be tested. The results obtained aim to facilitate early detection of glaucoma eyes. Accuracy on training data reaches 100% and accuracy in this study is reached 93.5484%.Keyword: Glaucoma, Morphological Operation, Thresholding, Artificial Neural Network AbstrakGlaukoma adalah penyakit mata yang disebabkan oleh peningkatan tekanan bola mata sehingga terjadi kerusakan saraf optik dan dapat menyebabkan kebutaan nomor dua setelah katarak. Kerusakan saraf sering terjadi tanpa gejala sehingga pemeriksaan dini dapat mengurangi resiko dari glaukoma. Oleh karena itu, penulis merancang suatu sistem untuk mendeteksi glaukoma melalui citra fundus mata dengan mengekstraksi beberapa fitur yaitu Rim to Disc Ratio, Cup to Disc Ratio (CDR), Vertical Cup to Disc Ratio (VCDR), Horizontal Cup to Disc Ratio (HCDR), dan Horizontal to Vertical CDR (H-V CDR) dengan mengsegmentasi Optic Disc (OD) dan Optic Cup (OC) dengan menggunakan metode Morphological Operations dan Thresholding. Artificial Neural Network (ANN) digunakan sebagai metode klasifikasi glaukoma. Melalui metode tersebut, data uji dapat dibagi dalam dua klasifikasi yaitu mata normal dan mata glaukoma. Data latih yang akan diambil sebanyak 62 buah dan data uji yang akan diambil sebanyak 62 buah. Hasil yang diperoleh bertujuan untuk memudahkan mendeteksi secara dini mata glaukoma. Akurasi pada data latih mencapai 100% dan akurasi pada data uji mencapai 93,5484%.Kata kunci: Glaukoma, Morphological Operation, Thresholding, Artificial Neural Network

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Jurnal Ilmiah FIFO

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