Diabetic Retinopathy Grading System Based on Transfer Learning

AbdelMaksoud, Eman; Barakat, Sherif; Elmogy, Mohammed

doi:10.48550/arxiv.2012.12515

Cited by 1 publication

(2 citation statements)

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“…Hence, CNN models that are pre-trained will be fine-tuned utilizing domain dataset. Two pre-trained models will be selected for this study, InceptionV3 and EfficientNetsB5, for their effectiveness in diagnosing DR in the work of [17], [8], [6], [11], [7]. The performance of the fine-tuned pre-trained models will be compared with the performance of a CNN without pre-training.…”

Section: Methodsmentioning

confidence: 99%

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Prediction of Diabetic Retinopathy using Convolutional Neural Networks

Alsuwat¹,

Alalawi²,

Alhazmi³

et al. 2022

IJACSA

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Diabetic retinopathy (DR) is among the most dangerous diabetic complications that can lead to lifelong blindness if left untreated. One of the essential difficulties in DR is early discovery, which is crucial for therapy progress. The accurate diagnosis of the DR stage is famously complicated and demands a skilled analysis by the expert being of fundus images. This paper detects DR and classifies its stage using retina images by applying conventional neural networks and transfer learning models. Three deep learning models were investigated: trained from scratch CNN and pre-trained InceptionV3 and Efficient-NetsB5. Experiment results show that the proposed CNN model outperformed the pre-trained models with a 9 to 25% relative improvement in F1-score compared to pre-trained InceptionV3 and EfficientNetsB5, respectively.

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Section: Methodsmentioning

confidence: 99%

“…As in the research, [17], [8], [6], [11], [7], this research aims to use InceptionV3, CNN, EfficientNetsB5 to detect DR due to their efficiency previous studies. However, these models will be validated using the same dataset to compare their results.…”

Section: Related Workmentioning

confidence: 99%