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IMPLEMENTASI KOMBINASI ARSITEKTUR VGG DAN SQUEEZENET UNTUK KLASIFIKASI PENYAKIT PADA MATA

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Penilaian

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Cataract, diabetic retinopathy (DR), and glaucoma are eye diseases that are at risk of blindness. Early detection of these diseases can be done by utilising deep learning methods. This research proposes a combined model of Visual Geometry Group (VGG) and SqueezeNet architecture with the addition of batch normalisation for eye disease classification. The convolution and max pooling layers of the VGG architecture are added in the first block to capture important features in the image in detail. SqueezeNet is added in the middle of the model to reduce the number of parameters and prevent overfitting. In the last block, the model uses fully connected layers and softmax activation function to provide prediction results. Batch normalisation is added to each convolution layer, before the SqueezeNet architecture and before the fully connected layer to help the model converge. The proposed model obtained an average accuracy of 99.5%, sensitivity of 99.2%, specificity of 99.46%, F1-Score of 99.62%, and cohen’s kappa of 78%. In this study, the proposed model works very well in recognising the DR class compared to cataract, glaucoma, and normal classes with a sensitivity value of 100%. Although the values obtained in cataract, glaucoma, and normal classes are still below DR, the results obtained are very good because the sensitivity values for cataract, glaucoma, and normal classes are above 90%. The cohen’s kappa value is still below 80% due to the low cohen’s kappa value in the cataract class, which is only 65%. For further research, architecture development can be carried out to overcome the cohen’s kappa value which is still below 80%.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2507001053	T167332	T1673322025	Central Library (Reference)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T1673322025

Publisher

: Prodi Ilmu Matematika, Fakultas Matematika Dan Ilmu Pengetahuan Alam Universitas Sriwijaya., 2025

Collation

xix, 63 hlm.; tab.; 29 cm.

Language

Indonesia

ISBN/ISSN

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Classification

510.07

Content Type

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Media Type

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Carrier Type

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Edition

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Subject(s)

Matematika

Specific Detail Info

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Statement of Responsibility

EM

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