Deep Learning for Diabetic Retinopathy Detection: Challenges and Opportunities

Mohan, N. Jagan; Murugan, R.; Goel, Tripti

doi:10.1007/978-981-19-2416-3_12

Cited by 12 publications

(3 citation statements)

References 44 publications

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“…After conducting a thorough literature review, we have discovered that using a sampling technique on the entire raw data may result in data leakage and ultimately lead to inflated metric values (Tampu et al, 2022;Silva et al, 2022;Jagan Mohan et al, 2022;Jamuna Devi and Kavitha, 2023;Navarro et al, 2021). However, when appropriately used, our strategy enhances the outcome of severely imbalanced data compared to the initial and existing results (Xie et al, 2019).…”

Section: Discussionmentioning

confidence: 99%

Diabetes Diagnosis through Machine Learning: Investigating Algorithms and Data Augmentation for Class Imbalanced BRFSS Dataset

Chowdhury,

Ayon,

Hossain

2023

Preprint

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Diabetes is a prevalent chronic condition that poses significant challenges to early diagnosis and identifying at-risk individuals. Machine learning plays a crucial role in diabetes detection by leveraging its ability to process large volumes of data and identify complex patterns. However, imbalanced data, where the number of diabetic cases is substantially smaller than non-diabetic cases, complicates the identification of individuals with diabetes using machine learning algorithms. Our study focuses on predicting whether a person is at risk of diabetes, considering the individual's health and socio-economic conditions while mitigating the challenges posed by imbalanced data. To minimize the impact of imbalance data, we employed several data augmentation techniques such as oversampling (SMOTE-N), undersampling (ENN), and hybrid sampling techniques (SMOTE-Tomek and SMOTE-ENN) on training data before applying machine learning algorithms. Our study sheds light on the significance of carefully utilizing data augmentation techniques, without any data leakage, in enhancing the effectiveness of machine learning algorithms. Moreover, it offers a complete machine learning structure for healthcare practitioners, from data obtaining to ML prediction, enabling them to make data-informed strategies.

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Section: Discussionmentioning

confidence: 99%

Diabetes Diagnosis through Machine Learning: Investigating Algorithms and Data Augmentation for Class Imbalanced BRFSS Dataset

Chowdhury,

Ayon,

Hossain

2023

Preprint

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show abstract

“…These algorithms anticipate optimal, exact outputs, simplifying early illness forecasts and saving millions of lives [8]. Computer vision and medical image processing applications, notably CNNs [8][9][10][11][12], have extensively used deep learning techniques.…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Model for Detecting the Diabetic Retinopathy Stages with Discrete Wavelet Transform

Mutawa,

Al-Sabti,

Raizada

et al. 2024

Preprint

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Diabetic retinopathy (DR) is the primary factor leading to vision impairment and blindness in diabetic people. Uncontrolled diabetes can damage the retinal blood vessels. Initial detection and prompt medical intervention are vital in preventing progressive vision impairment. Today's growing medical field poses more significant workload and diagnostic demands on medical professionals. In the proposed study, a convolutional neural network (CNN) is employed to detect the stages of DR. This research is crucial for studying DR because of its innovative methodology incorporating two different public datasets. This strategy enhances the model's capacity to generalize unseen DR images, as each dataset encompasses unique demographics and clinical circumstances. The network can learn and capture complicated hierarchical image features with asymmetric weights. Each image undergoes preprocessing using contrast-limited adaptive histogram equalization (CLAHE) and the discrete wavelet transform (DWT). The model is trained and validated using the combined datasets of Dataset for Diabetic Retinopathy (DDR) and the Asia Pacific Tele-Ophthalmology Society (APTOS). The CNN model is tuned with different learning rates and optimizers. The CNN model with Adam optimizer achieved 70% accuracy and an area under curve (AUC) score of 0.82. The recommended study results may reduce diabetes-related vision impairment by early DR severity identification.

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“…These algorithms anticipate optimal, exact outputs, simplifying early illness forecasts and saving millions of lives [8]. Computer vision and medical image processing applications extensively use deep learning techniques, notably convolutional neural networks (CNNs) [8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Model for Detecting Diabetic Retinopathy Stages with Discrete Wavelet Transform

Mutawa,

Al-Sabti,

Raizada

et al. 2024

Applied Sciences

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Diabetic retinopathy (DR) is the primary factor leading to vision impairment and blindness in diabetics. Uncontrolled diabetes can damage the retinal blood vessels. Initial detection and prompt medical intervention are vital in preventing progressive vision impairment. Today’s growing medical field presents a more significant workload and diagnostic demands on medical professionals. In the proposed study, a convolutional neural network (CNN) is employed to detect the stages of DR. This research is crucial for studying DR because of its innovative methodology incorporating two different public datasets. This strategy enhances the model’s capacity to generalize unseen DR images, as each dataset encompasses unique demographics and clinical circumstances. The network can learn and capture complicated hierarchical image features with asymmetric weights. Each image is preprocessed using contrast-limited adaptive histogram equalization and the discrete wavelet transform. The model is trained and validated using the combined datasets of Dataset for Diabetic Retinopathy and the Asia-Pacific Tele-Ophthalmology Society. The CNN model is tuned in with different learning rates and optimizers. An accuracy of 72% and an area under curve score of 0.90 was achieved by the CNN model with the Adam optimizer. The recommended study results may reduce diabetes-related vision impairment by early identification of DR severity.

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Deep Learning for Diabetic Retinopathy Detection: Challenges and Opportunities

Cited by 12 publications

References 44 publications

Diabetes Diagnosis through Machine Learning: Investigating Algorithms and Data Augmentation for Class Imbalanced BRFSS Dataset

Diabetes Diagnosis through Machine Learning: Investigating Algorithms and Data Augmentation for Class Imbalanced BRFSS Dataset

A Deep Learning Model for Detecting the Diabetic Retinopathy Stages with Discrete Wavelet Transform

A Deep Learning Model for Detecting Diabetic Retinopathy Stages with Discrete Wavelet Transform

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