A new model for retinal lesion detection of diabetic retinopathy using hierarchical self-organizing maps

Zadeh, Hossein Ghayoumi; Jamshidi, Hamidreza; Fayazi, Ali; Gholizadeh, Mohammad Hossein; Toussi, Cyrus Ahmadi; Danaeian, Mostafa

doi:10.1007/s42044-019-00041-2

Cited by 2 publications

(2 citation statements)

References 15 publications

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“…Diabetes is a condition in which blood flow is obstructed throughout the body. The retinal blood vessels may leak in this disorder, resulting in retinal edema [ 79 ]. Four learning strategies for extracting patterns from data have been described based on data types: supervised, semi-supervised, unsupervised, and reinforced.…”

Section: Resultsmentioning

confidence: 99%

Hybrid stacked ensemble combined with genetic algorithms for diabetes prediction

Abdollahi

Nouri-Moghaddam

2022

Iran J Comput Sci

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Diabetes is currently one of the most common, dangerous, and costly diseases globally caused by increased blood sugar or a decrease in insulin in the body. Diabetes can have detrimental effects on people’s health if diagnosed late. Today, diabetes has become one of the challenges for health and government officials. Prevention is a priority, and taking care of people’s health without compromising their comfort is an essential need. In this study, the ensemble training methodology based on genetic algorithms was used to diagnose and predict the outcomes of diabetes mellitus accurately. This study uses the experimental data, actual data on Indian diabetics on the University of California website. Current developments in ICT, such as the Internet of Things, machine learning, and data mining, allow us to provide health strategies with more intelligent capabilities to accurately predict the outcomes of the disease in daily life and the hospital and prevent the progression of this disease and its many complications. The results show the high performance of the proposed method in diagnosing the disease, which has reached 98.8%, and 99% accuracy in this study.

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Section: Resultsmentioning

confidence: 99%

Hybrid stacked ensemble combined with genetic algorithms for diabetes prediction

Abdollahi

Nouri-Moghaddam

2022

Iran J Comput Sci

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“…They design lightweight networks called SI2DRNet-vl. Zadeh et al [14] presented a classifier with Hierarchical Self Organizing Maps which increased the accuracy and classifier speed. S.Long et al [15] presented a classifier with a Support Vector Machine for lesion prediction, which produces good accuracy.…”

Section: B Deep Learning Algorithmmentioning

confidence: 99%

Dense Convolution Neural Network for Automated Diabetic Retinopathy Detection

K. Geethalakshmi, Dr. V. S. Meenakshi

2023

tjjpt

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Diabetic Retinopathy is a diabetes complication due to uncontrolled levels of glucose in the body which leads to abnormality in the eyes which owing to distortion in the retina and leading to permanent damage of the eye or vision losses. Hence identification and classification of diabetic retinopathy through manual observation is a challenging process due to composite boundaries and features with a high degree of intraclass variation and a low degree of interclass variation. Unsupervised machine learning algorithms have been used to classify diseases automatically based on the appearance of lesions and their characteristics. These models require more processing time and less reliability. It has been proposed that deep learning architecture can overcome these limitations as it is more efficient and accurate at detecting the lesion’s features. The novel dense convolution neural network has been proposed with preprocessing, segmentation, feature extraction and classification steps. Laplacian filter and CLAHE techniques have been used in a preprocessing step. The region growing algorithm, Principal Component Analysis and Dense CNN have been used for segmentation, Feature extraction and classification of DR lesions. Furthermore, the proposed model was compared with conventional classifiers in terms of Accuracy parameters. The proposed model achieves 97.88 % of Accuracy. It improves computing efficiency and minimizes network complexity. Hence the proposed model can accurately detect the lesions in the retinal fundus images.

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A new model for retinal lesion detection of diabetic retinopathy using hierarchical self-organizing maps

Cited by 2 publications

References 15 publications

Hybrid stacked ensemble combined with genetic algorithms for diabetes prediction

Hybrid stacked ensemble combined with genetic algorithms for diabetes prediction

Dense Convolution Neural Network for Automated Diabetic Retinopathy Detection

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