Detection of Cataract Based on Image Features Using Convolutional Neural Networks
Cataract are the highest cause of blindness that there are 32.4 million people experiencing blindness and as many as 191 million people experiencing visual disabilities in 2010 in the world. On the other hand, the longer a patient suffers from cataracts or late treatment. The development of cataract...
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| Format: | Article |
| Language: | English |
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Universitas Gadjah Mada
2021-01-01
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| Series: | IJCCS (Indonesian Journal of Computing and Cybernetics Systems) |
| Subjects: | |
| Online Access: | https://jurnal.ugm.ac.id/ijccs/article/view/61882 |
| _version_ | 1829139794100224000 |
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| author | Indra Weni Pradita Eko Prasetyo Utomo Benedika Ferdian Hutabarat Muksin Alfalah |
| author_facet | Indra Weni Pradita Eko Prasetyo Utomo Benedika Ferdian Hutabarat Muksin Alfalah |
| author_sort | Indra Weni |
| collection | DOAJ |
| description | Cataract are the highest cause of blindness that there are 32.4 million people experiencing blindness and as many as 191 million people experiencing visual disabilities in 2010 in the world. On the other hand, the longer a patient suffers from cataracts or late treatment. The development of cataract identification using a traditional algorithm based on feature representation is highly dependent on the classification process carried out by an eye specialist so that the method is prone to misclassification of a person detected or not. However, at this time there is a deep learning, convolutional neural network (CNN) which is used for pattern recognition which can help automate image classification. This research was conducted to increase the accuracy value and minimize data loss in the process of cataract identification by performing an experience namely the manipulation process was carried out by changing epochs. The results of this study indicate that the addition of epochs affects accuracy and loss data from CNN. By comparing variety of epoch values it can be ignored that the higher the age values used, the higher the value of the model. In this study, using the epoch 50 value reached the highest value with a value of 95%. Based on the model that has been made it has also been successful to receive images according to the specified class. After testing accurately, 10 images achieved an average accuracy of 88%. |
| first_indexed | 2024-12-14T19:41:54Z |
| format | Article |
| id | doaj.art-80e71af1ac904762b012d30b05e1942c |
| institution | Directory Open Access Journal |
| issn | 1978-1520 2460-7258 |
| language | English |
| last_indexed | 2024-12-14T19:41:54Z |
| publishDate | 2021-01-01 |
| publisher | Universitas Gadjah Mada |
| record_format | Article |
| series | IJCCS (Indonesian Journal of Computing and Cybernetics Systems) |
| spelling | doaj.art-80e71af1ac904762b012d30b05e1942c2022-12-21T22:49:42ZengUniversitas Gadjah MadaIJCCS (Indonesian Journal of Computing and Cybernetics Systems)1978-15202460-72582021-01-01151758610.22146/ijccs.6188229260Detection of Cataract Based on Image Features Using Convolutional Neural NetworksIndra Weni0Pradita Eko Prasetyo Utomo1Benedika Ferdian Hutabarat2Muksin Alfalah3Department of Information Systems, FST Universitas Jambi, JambiDepartment of Information Systems, FST Universitas Jambi, JambiDepartment of Information Systems, FST Universitas Jambi, JambiDepartment of Information Systems, FST Universitas Jambi, JambiCataract are the highest cause of blindness that there are 32.4 million people experiencing blindness and as many as 191 million people experiencing visual disabilities in 2010 in the world. On the other hand, the longer a patient suffers from cataracts or late treatment. The development of cataract identification using a traditional algorithm based on feature representation is highly dependent on the classification process carried out by an eye specialist so that the method is prone to misclassification of a person detected or not. However, at this time there is a deep learning, convolutional neural network (CNN) which is used for pattern recognition which can help automate image classification. This research was conducted to increase the accuracy value and minimize data loss in the process of cataract identification by performing an experience namely the manipulation process was carried out by changing epochs. The results of this study indicate that the addition of epochs affects accuracy and loss data from CNN. By comparing variety of epoch values it can be ignored that the higher the age values used, the higher the value of the model. In this study, using the epoch 50 value reached the highest value with a value of 95%. Based on the model that has been made it has also been successful to receive images according to the specified class. After testing accurately, 10 images achieved an average accuracy of 88%.https://jurnal.ugm.ac.id/ijccs/article/view/61882cataractconvolutional neural networkepochimageaccuracy |
| spellingShingle | Indra Weni Pradita Eko Prasetyo Utomo Benedika Ferdian Hutabarat Muksin Alfalah Detection of Cataract Based on Image Features Using Convolutional Neural Networks IJCCS (Indonesian Journal of Computing and Cybernetics Systems) cataract convolutional neural network epoch image accuracy |
| title | Detection of Cataract Based on Image Features Using Convolutional Neural Networks |
| title_full | Detection of Cataract Based on Image Features Using Convolutional Neural Networks |
| title_fullStr | Detection of Cataract Based on Image Features Using Convolutional Neural Networks |
| title_full_unstemmed | Detection of Cataract Based on Image Features Using Convolutional Neural Networks |
| title_short | Detection of Cataract Based on Image Features Using Convolutional Neural Networks |
| title_sort | detection of cataract based on image features using convolutional neural networks |
| topic | cataract convolutional neural network epoch image accuracy |
| url | https://jurnal.ugm.ac.id/ijccs/article/view/61882 |
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