Automatic recognition of retinopathy diseases by using wavelet based neural network

Yağmur, Fatih; Karlık, Bekir; Okatan, A.

doi:10.1109/icadiwt.2008.4664391

Cited by 12 publications

(3 citation statements)

References 3 publications

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“…Image segmentation was carried out by Cornforth et al [46], who employed a combination of wavelet assessment, supervised classifier likelihoods, adaptive threshold methods, and morphology-based approaches. Different retinal diseases were monitored by Yagmur et al [47] by incorporating the DWT in image processing. Another study by Rehman et al [48] applied DWT with different machine learning classifiers, such as k-nearest neighbors (KNN) and support vector machine (SVM) models.…”

Section: Literature Reviewmentioning

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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Section: Literature Reviewmentioning

confidence: 99%

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

Mutawa,

Al-Sabti,

Raizada

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Images were segmented by Cornforth et al [46], who combined the wavelet assessment, supervised classifier likelihoods, adaptive threshold methods, and morphology-based approaches. Different retinal diseases were monitored by Yagmur et al [47] by incorporating DWT in image processing. Another study by Rehman et al [48] applied DWT with different machine learning classifiers, such as knearest neighbors (KNN) and support vector machine (SVM) models.…”

Section: Literature Reviewmentioning

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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“…In ophthalmology, a detailed eye fundus examination is highly preferred for diagnosing the abnormalities with the normal images (Yagmur et al, 2008), the problem for the specialist's lies in processing the huge data; only then can a meaningful diagnosis be made to reveal the abnormalities. An automated system can overcome this problem by automatically identifying and classifying all the images.…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach for Diagnosing Diabetic Retinopathy in Fundus Images

Dhiravidachelvi¹,

Rajamani²

2015

Journal of Computer Science

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Abstract:In recent years the medical profession has seen an ever increasing demand for diagnosis and a permanent cure for illnesses caused by climatic-changes, unwholesome food and environmental pollution. Also the appearances of hitherto unknown viral diseases have caused eye diseases, which have prompted surgeons to monitor the health of the eyes. Potential new therapies that may help in preserving sight in the growing population of diabetic patients into the 21st Century. Early detection of diseases affecting the eyes reduces the risk of permanent damage. Some of the serious conditions which warrant early diagnosis are: Glaucoma, floaters, macula degradation and diabetic retinopathy. In the early stages, a choice of treatment options exist, which dwindles as the disease spreads. A visual inspection of the optic disc, macula and the blood vessels of the eye requires to be done routinely. Diabetic patients run the risk of damage to retinal vessels, which are referred to as diabetic retinopathy. This may further be classified as: Non-proliferative diabetic retinopathy and proliferative diabetic retinopathy. In scientific literatures, feature extraction method has been reported for diagnosis and classification. In this study a systematic Decide, Detect, Determine and Do approach for analyzing diabetic retinopathy images has been taken up. The proposed approach gives a clearer picture of the abnormality, its type (NPDR or PDR), its status (viz., mild, moderate or severe) and finally the appropriate treatment.

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Automatic recognition of retinopathy diseases by using wavelet based neural network

Cited by 12 publications

References 3 publications

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

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

A Novel Approach for Diagnosing Diabetic Retinopathy in Fundus Images

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