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PENERAPAN DEEP LEARNING UNTUK KLASIFIKASI PENYAKIT MATA MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK (CNN): ARSITEKTUR DENSENET 121 DAN XCEPTION

Sari, Yuni Kartika - Personal Name;

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Retina diseases are a major cause of vision problems that can lead to a loss of sight or even blindness if not detected early. Other eye conditions, like cataracts and glaucoma, can also severely impair vision if not treated properly. This research aims to classify retina images using a Convolutional Neural Network (CNN) approach by comparing two architectures: DenseNet 121 and Xception. DenseNet 121 is known for its parameter efficiency and strong inter-layer connections, while Xception optimizes the depthwise separable convolution process. Test results showed that both models achieved the same high accuracy of 99.05%. However, DenseNet 121 proved to be more efficient in training, taking only 1 second per step, compared to Xception, which required 18 seconds per step. Based on these findings, DenseNet 121 is the best model in this study because it achieves high accuracy with a much shorter training time. Applying this model is expected to support the faster and more accurate detection of retina diseases, thereby aiding medical diagnosis. Keywords: Classification, Retina, Convolutional neural network (CNN), DenseNet 121, Xception, Eye disease, Medical image.

Availability

Inventory Code	Barcode	Call Number	Location	Status
2507006275	T185641	T1856412025	Central Library (Reference)	Available but not for loan - Not for Loan

Detail Information

Series Title: -
Call Number: T1856412025
Publisher: Indralaya : Prodi Sistem Komputer, Fakultas Ilmu Komputer Universitas Sriwijaya., 2025
Collation: xiv, 100 hlm.; ilus.; tab.; 29 cm.
Language: Indonesia
ISBN/ISSN: -
Classification: 617.710 7
Content Type: Text
Media Type: unmediated
Carrier Type: other (computer)
Edition: -
Subject(s): Penyakit pada Mata
Prodi Sistem Komputer
Specific Detail Info: -
Statement of Responsibility: MI

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IMPLEMENTASI METODE CONVOLUTIONAL NEURAL NETWORK UNTUK MEMPROSES FACE RECOGNIZER PADA ROBOT MOBILE		id
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IMPLEMENTASI KOMBINASI ARSITEKTUR VGG DAN SQUEEZENET UNTUK KLASIFIKASI PENYAKIT PADA MATA		id

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PENERAPAN DEEP LEARNING UNTUK KLASIFIKASI PENYAKIT MATA MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK (CNN): ARSITEKTUR DENSENET 121 DAN XCEPTION

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