Development of Mobile Application for Detection and Grading of Diabetic Retinopathy

Kipli, Kuryati; Hui, Lee Yee; Tajudin, Nurul Mirza Afiqah; Sapawi, Rohana; Sahari, Siti Kudnie; Mat, Dayang Azra Awang; Jalil, M. A.; Ray, Kanad; Kaiser, M. Shamim; Mahmud, Mufti

doi:10.1007/978-981-16-8826-3_29

Cited by 4 publications

(2 citation statements)

References 1 publication

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“…One significant contribution would involve the development of an application that can assist medical experts and even patients in the early detection of diabetic retinopathy. Such an application would not only prevent vision loss but also save valuable therapy time and costs [ 75 ]. By leveraging the power of deep learning models such as DenseNet 121, this application could provide efficient and reliable diagnoses, benefiting individuals at risk of developing diabetic retinopathy.…”

Section: Discussionmentioning

confidence: 99%

Using Deep Learning Architectures for Detection and Classification of Diabetic Retinopathy

Mohanty

Mahapatra²,

Acharya

et al. 2023

Sensors

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Diabetic retinopathy (DR) is a common complication of long-term diabetes, affecting the human eye and potentially leading to permanent blindness. The early detection of DR is crucial for effective treatment, as symptoms often manifest in later stages. The manual grading of retinal images is time-consuming, prone to errors, and lacks patient-friendliness. In this study, we propose two deep learning (DL) architectures, a hybrid network combining VGG16 and XGBoost Classifier, and the DenseNet 121 network, for DR detection and classification. To evaluate the two DL models, we preprocessed a collection of retinal images obtained from the APTOS 2019 Blindness Detection Kaggle Dataset. This dataset exhibits an imbalanced image class distribution, which we addressed through appropriate balancing techniques. The performance of the considered models was assessed in terms of accuracy. The results showed that the hybrid network achieved an accuracy of 79.50%, while the DenseNet 121 model achieved an accuracy of 97.30%. Furthermore, a comparative analysis with existing methods utilizing the same dataset revealed the superior performance of the DenseNet 121 network. The findings of this study demonstrate the potential of DL architectures for the early detection and classification of DR. The superior performance of the DenseNet 121 model highlights its effectiveness in this domain. The implementation of such automated methods can significantly improve the efficiency and accuracy of DR diagnosis, benefiting both healthcare providers and patients.

show abstract

Section: Discussionmentioning

confidence: 99%

Using Deep Learning Architectures for Detection and Classification of Diabetic Retinopathy

Mohanty

Mahapatra²,

Acharya

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…One signifi-cant contribution would involve the development of an application that can assist medical experts and even patients in the early detection of diabetic retinopathy. Such an application would not only prevent vision loss but also save valuable therapy time and costs [75]. By supporting the power of deep learning models such as DenseNet 121, this application could provide efficient and reliable diagnoses, benefiting individuals at risk of developing diabetic retinopathy.…”

Section: Discussionmentioning

confidence: 99%

The Detection and Classification of Diabetic Retinopathy using the Architectures of Deep Learning

2023

IJFMR

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Diabetic retinopathy is a severe problem of long-term diabetes, which affects the human eye and gradually leading to permanent blindness. The early detection of DR is crucial for effective treatment, as symptoms often manifest in later stages. The manual grading of retinal images is time-consuming, prone to errors, and lacks patient-friendliness. In this study, we propose two deep learning (DL) architectures, a hybrid network combining VGG16 and XGBoost Classi- fier, and the DenseNet 121 network, for Diabetic retionopathy detection and classification. To evaluate the two DL models, we preprocessed a collection of retinal images obtained from the APTOS 2019 Blindness Detection Kaggle Dataset. This dataset exhibits not balanced image class distribution, which we addressed through appropriate balancing techniques. The performance of the considered models was assessed in terms of accuracy. The results showed that the hybrid network achieved an accuracy of 79.50%, while the DenseNet 121 model achieved an accuracy of 97.30%. Furthermore, a comparative analysis with existing methods utilizing the same dataset revealed the superior performance of the DenseNet 121 network. The findings of this study demonstrate the potential of DL architectures for the early detection and classification of DR. The better performance of the DenseNet 121 model highlights its effectiveness in this domain. The implementation of such automated methods can sig- nificantly improve the efficiency and accuracy of DR diagnosis, benefiting both healthcare providers and patients.

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Performance of Automated Machine Learning for Diabetic Retinopathy Image Classification from Multi-field Handheld Retinal Images

Jacoba

Doan

Salongccay

et al. 2023

Ophthalmology Retina

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Development of Mobile Application for Detection and Grading of Diabetic Retinopathy

Cited by 4 publications

References 1 publication

Using Deep Learning Architectures for Detection and Classification of Diabetic Retinopathy

Using Deep Learning Architectures for Detection and Classification of Diabetic Retinopathy

The Detection and Classification of Diabetic Retinopathy using the Architectures of Deep Learning

Performance of Automated Machine Learning for Diabetic Retinopathy Image Classification from Multi-field Handheld Retinal Images

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