An active deep learning method for diabetic retinopathy detection in segmented fundus images using artificial bee colony algorithm

Özbay, Erdal

doi:10.1007/s10462-022-10231-3

Cited by 59 publications

(19 citation statements)

References 76 publications

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“… 27 , 28 For example, one of the latest technologies in AI for DR detection was the development of an active deep learning (ADL) method using an artificial bee colony (ABC) algorithm. This method has been shown to have enhanced ability to detect five levels of DR severity whilst an earlier AI method was only able to detect two DR levels: referrable and non-referrable DR. 29 These results indicated its potential in clinical scenario to enhance efficiency in DR screening coverage. However, much attention has mostly focused on the development of a DR screening system which involved the use of sophisticated or expensive equipment that are less portable and less affordable for low resource countries.…”

Section: Discussionmentioning

confidence: 80%

Accuracy of Low-Cost, Smartphone-Based Retinal Photography for Diabetic Retinopathy Screening: A Systematic Review

Prayogo,

Zaharo,

Damayanti

et al. 2023

OPTH

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Diabetic retinopathy (DR) is a leading cause of blindness. Early DR screening is essential, but the infrastructure can be less affordable in low resource countries. This study aims to review the accuracy of low-cost smartphone-based fundus cameras for DR screening in adult patients with diabetes. Methods: We performed a systematic literature search to find studies that reported the sensitivity and specificity of low-cost smartphone-based devices for fundus photography in adult patients with diabetes. We searched three databases (MEDLINE, Google Scholar, Scopus) and one register (Cochrane CENTRAL). We presented the accuracy values by grouping the diagnosis into three: any DR, referrable DR, and diabetic macular oedema (DMO). Risk of bias and applicability of the studies were assessed using QUADAS-2. Results: Five out of 294 retrieved records were included with a total of six smartphone-based devices reviewed. All of the reference diagnostic methods used in the included studies were either indirect ophthalmoscopy or slit-lamp examinations and all smartphonebased devices' imaging protocols used mydriatic drops. The reported sensitivity and specificity for any DR were 52-92.2% and 73.3-99%; for referral DR were 21-91.4% and 64.9-100%; and for DMO were 29.4-81% and 95-100%, respectively. Conclusion: Sensitivity available low-cost smartphone-based devices for DR screening were acceptable and their specificity particularly for detecting referrable DR and DMO were considerably good. These findings support their potential utilization for DR screening in a low resources setting.

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Section: Discussionmentioning

confidence: 80%

Accuracy of Low-Cost, Smartphone-Based Retinal Photography for Diabetic Retinopathy Screening: A Systematic Review

Prayogo,

Zaharo,

Damayanti

et al. 2023

OPTH

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“…Different evaluation metrics were used to measure the performance of the models used in the study to diagnose tonsillopharyngitis disease using oropharyngeal images. Accuracy, Sensitivity, Specificity, Negative Predictive Value(NPV), False Positive Rate(FPR), False Negative Rate(FNR), False Discovery Rate(FDR), F1 score, and Matthews Correlation Coefficient(MCC) are the leading performance measurement metrics used in the study [28][29][30].…”

Section: Resultsmentioning

confidence: 99%

Detection of Tonsillopharyngitis with Grad-Cam and Optimization-Based Model Using Oropharyngeal Images

Yıldırım

Eroğlu

2022

NATURENGS MTU Journal of Engineering and Natural Sciences Malatya Turgut Ozal University

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Tonsillopharyngitis is a sudden onset contagious infection of the pharynx and tonsils. Patients experience a rapid general condition and loss of workforce. In addition to affecting patients, it spreads and affects other individuals. In addition, it causes severe complications and increases hospital costs. Therefore, early and accurate diagnosis is essential. In this study, a hybrid model is developed for the diagnosis of tonsillopharyngitis. First, the heat maps of the images in the original data set by applying the Gradient-weighted Class Activation Mapping (Grad-Cam) method. In the proposed model, feature maps are obtained from the original and heatmap datasets using the Darknet53 architecture as the base. It is aimed to increase the performance of the proposed model by bringing together different features of the same image. After the feature map obtained after the feature fusion step is optimized with the Relief method, classification is carried out using an SVM shallow classifier.

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“…Today, artificial intelligence and machine learning techniques are used by many researchers in different subjects such as biomedical image processing, text, and voice analysis [54][55][56]. In this study, a method is proposed to diagnose AD, which negatively affects the quality of life, according to the severity of dementia using Alzheimer's MRIs.…”

Section: Discussionmentioning

confidence: 99%

An NCA-based Hybrid CNN Model for Classification of Alzheimer’s Disease on Grad-CAM-enhanced Brain MRI Images

Özbay

2023

Turkish Journal of Science and Technology

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Alzheimer’s, one of the most prevalent varieties of dementia, is a fatal neurological disease for which there is presently no known cure. Early diagnosis of such diseases and classification with computer-aided systems are of great importance in determining the most appropriate treatment. Imaging the soft tissue of the brain with Magnetic Resonance Imaging (MRI) and revealing specific findings is the most effective method of Alzheimer’s diagnosis. A few recent studies using Deep Learning (DL) to diagnose Alzheimer’s Disease (AD) with brain MRI scans have shown promising results. However, the fundamental issue with DL architectures like CNN is the amount of training data that is required. In this study, a hybrid CNN method based on Neighborhood Component Analysis (NCA) is proposed, which aims to classify AD over brain MRI with Machine Learning (ML) algorithms. According to the classification results, DenseNet201, EfficientNet-B0, and AlexNet pre-trained CNN architectures, which are 3 architectures that give the best results as feature extractors, were used as hybrids among 10 different DL architectures. By means of these CNN architectures, the features trained on the dataset and the features obtained by Gradient-weighted Class Activation Mapping (Grad-CAM) are concatenated. The NCA method has been used to optimize all concatenated features. After the stage, the optimized features have been classified with KNN, Ensemble, and SVM algorithms. The proposed hybrid model achieved 99.83% accuracy, 99.88% sensitivity, 99.92% specificity, 99.83% precision, 99.85% F1-measure, and 99.78% Matthews Correlation Coefficient (MCC) results using the Ensemble classifier for the 4-class classification of AD.

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An active deep learning method for diabetic retinopathy detection in segmented fundus images using artificial bee colony algorithm

Cited by 59 publications

References 76 publications

Accuracy of Low-Cost, Smartphone-Based Retinal Photography for Diabetic Retinopathy Screening: A Systematic Review

Accuracy of Low-Cost, Smartphone-Based Retinal Photography for Diabetic Retinopathy Screening: A Systematic Review

Detection of Tonsillopharyngitis with Grad-Cam and Optimization-Based Model Using Oropharyngeal Images

An NCA-based Hybrid CNN Model for Classification of Alzheimer’s Disease on Grad-CAM-enhanced Brain MRI Images

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