Detection of Cataract Based on Image Features Using Convolutional Neural Networks

Weni, Indra and Utomo, Pradita Eko Prasetyo and Hutabarat, Benedika Ferdian and Alfalah, Muksin (2021) Detection of Cataract Based on Image Features Using Convolutional Neural Networks. Indonesian Journal of Computing and Cybernetics Systems), 15 (1). pp. 75-86. ISSN 2460-7258

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Abstract

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 fromcataracts or late treatment. The development of cataract identification usinga 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 experiencenamely the manipulation process was carried out by changing epochs. The results of this study indicate that the addition of epochs affects accuracy and lossdata from CNN. By comparing varietyof 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%

Type:	Article
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software
Divisions:	Fakultas Sains dan Teknologi > Sistem Informasi
Depositing User:	Utomo
Date Deposited:	23 Mar 2021 07:57
Last Modified:	23 Mar 2021 07:57
URI:	https://repository.unja.ac.id/id/eprint/16965

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