Comparing Different Preprocessing Techniques for the Classification Tasks in Diabetic Retinopathy from Fundus Images

Mukherjee, Nilarun; Sengupta, Souvik

doi:10.1007/978-981-16-5207-3_51

Cited by 4 publications

(3 citation statements)

References 15 publications

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“…To reduce such data heterogeneity, which ultimately affects the performance of the classification model, as well as to highlight some fine details of the images, preprocessing methods of the fundus images were successfully introduced in various studies. As can be seen in many works, the best preprocessing method strongly depends on the combination of the characteristics of the used dataset and the proposed model [28][29][30][31][32][33].…”

Section: Related Workmentioning

confidence: 99%

“…Different preprocessing methods accentuate different image details. It may vary depending on the characteristics of used fundus dataset [28][29][30][31][32], described above in Related Works. For this reason and to achieve diverse data suitable for ensemble classification, we applied four different types of preprocessing on the original images.…”

Section: Preprocessing Of Imagesmentioning

confidence: 99%

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Image preprocessing‐based ensemble deep learning classification of diabetic retinopathy

Macsik,

Pavlovicova,

Kajan

et al. 2023

IET Image Processing

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Diabetic retinopathy (DR) can cause irreversible eye damage, even blindness. The prognosis improves with early diagnosis. According to the International Classification of Diabetic Retinopathy Severity Scale (ICDRSS), DR has five stages. Modern, cost‐effective techniques for automatic DR screening and staging of fundus images are based on deep learning (DL). To obtain higher classification accuracy, the combination of several diverse individual DL models into one ensemble could be used. A new approach to model diversity in an ensemble is proposed by manipulating the training input data involving original and four variants of preprocessed image datasets. There are publicly available datasets with labels for all five stages, but some contain poor‐quality images. In contrast, this algorithm was trained on images from a six‐class DDR dataset, including the class of poor‐quality ungradable images, to enhance the classification performance. The solution was evaluated on the APTOS dataset, containing only ICDRSS classes. Classification results of the ensemble model were presented on two different ensemble convolutional neural network (CNN) models, based on Xception and EfficientNetB4 architectures using two fusion approaches. Our proposed ensemble models outperformed all other single deep learning architectures regarding overall accuracy and Cohen's Kappa, with the best results using the EfficientNetB4 architecture.

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Section: Related Workmentioning

confidence: 99%

Section: Preprocessing Of Imagesmentioning

confidence: 99%

Image preprocessing‐based ensemble deep learning classification of diabetic retinopathy

Macsik,

Pavlovicova,

Kajan

et al. 2023

IET Image Processing

View full text Add to dashboard Cite

show abstract

“…To avoid vision loss and enhance patient outcomes, DR must be identified early and treated promptly. The manual grading of fundus pictures used in current DR screening procedures is time-consuming, expensive, and vulnerable to inter-and intra-observer variability [2]. Therefore, there is a need for automated screening techniques that can quickly and affordably detect and classify DR.…”

Section: Introductionmentioning

confidence: 99%

Diabetic Retinopathy Classification Using Deep Learning

Sathwik,

Agarwal,

Jubilson E

et al. 2023

EAI Endorsed Trans Perv Health Tech

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One of the main causes of adult blindness and a frequent consequence of diabetes is diabetic retinopathy (DR). To avoid visual loss, DR must be promptly identified and classified. In this article, we suggest an automated DR detection and classification method based on deep learning applied to fundus pictures. The suggested technique uses transfer learning for classification. On a dataset of 3,662 fundus images with real-world DR severity labels, we trained and validated our model. According to our findings, the suggested technique successfully detected and classified DR with an overall accuracy of 78.14%. Our model fared better than other recent cutting-edge techniques, illuminating the promise of deep learning-based strategies for DR detection and management. Our research indicates that the suggested technique may be employed as a screening tool for DR in a clinical environment, enabling early illness diagnosis and prompt treatment.

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Comparing Deep Feature Extraction Strategies for Diabetic Retinopathy Stage Classification from Fundus Images

Mukherjee¹,

Sengupta

2023

Arab J Sci Eng

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Comparing Different Preprocessing Techniques for the Classification Tasks in Diabetic Retinopathy from Fundus Images

Cited by 4 publications

References 15 publications

Image preprocessing‐based ensemble deep learning classification of diabetic retinopathy

Image preprocessing‐based ensemble deep learning classification of diabetic retinopathy

Diabetic Retinopathy Classification Using Deep Learning

Comparing Deep Feature Extraction Strategies for Diabetic Retinopathy Stage Classification from Fundus Images

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