Classification of OCT Images via Deep Learning

Güldemir, Numan Halit; Alkan, Ahmet

doi:10.35234/fumbd.900079

Cited by 2 publications

(1 citation statement)

References 11 publications

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“…Age Related Macular Degeneration (AMD) disease was detected using Xception, VGG16, InceptionV3 and ResNet50 deep neural network models trained with Convolutional Neural Network (CNN) architecture [4]. In another study, Tasmin et al, suggested the use of MobileNetV2, ResNet50 and Xception deep neural network models in the classification of normal, Drusen, AMD, Diabetic Macular Edema (DME) of the retina based on CNN architecture using OCT images [5].…”

Section: Güldemir Et Al In Their Study In 2021 Oct-optical Coherence ...mentioning

confidence: 99%

Utilizing Deep Learning and Data Augmentation for Early Detection of Eye Diseases in Pets

Şengöz

2023

International Journal of Engineering and Innovative Research

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This paper presents a deep learning algorithm for the diagnosis of eye diseases, which is taken from cats and dogs, using data augmentation. The database of eye images was collected from cell phone cameras, and with data augmentation techniques were used to increase the number of samples. The performance of the algorithms was evaluated on the original dataset of 146 diseased and 255 healthy images. The results showed that the VGG16 algorithm achieved a classification accuracy of 99.25% before data augmentation, which was significantly higher than the accuracy of existing methods. Furthermore, after the data augmentation again VGG16 model has significant performance metrics that are 99.9% than other algorithms. The proposed algorithm can be used to accurately diagnose various eye diseases, which can potentially improve the quality of care for patients.

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Section: Güldemir Et Al In Their Study In 2021 Oct-optical Coherence ...mentioning

confidence: 99%