Uncertainty-aware diabetic retinopathy detection using deep learning enhanced by Bayesian approaches

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Jan. 8, 2025

Abstract Deep learning-based medical image analysis has shown strong potential in disease categorization, segmentation, detection, and even prediction. However, high-stakes complex domains like healthcare, the opaque nature of these models makes it challenging to trust predictions, particularly uncertain cases. This sort uncertainty can be crucial analysis; diabetic retinopathy is an example where slight errors without indication confidence have adverse impacts. Traditional deep learning rely on single-point limiting their ability provide measures essential for robust clinical decision-making. To solve this issue, Bayesian approximation approaches evolved are gaining market traction. In work, we implemented a transfer approach, building upon DenseNet-121 convolutional neural network detect retinopathy, followed by extensions trained model. techniques, including Monte Carlo Dropout, Mean Field Variational Inference, Deterministic were applied represent posterior predictive distribution, allowing us evaluate model predictions. Our experiments combined dataset (APTOS 2019 + DDR) with pre-processed images showed that Bayesian-augmented outperforms state-of-the-art test accuracy, achieving 97.68% Dropout model, 94.23% 91.44% We also measure how certain predictions are, using entropy standard deviation metric each approach. evaluated both AUC accuracy scores at multiple data retention levels. addition overall performance boosts, results highlight does not only improve classification detection but reveals beneficial insights about estimation help build more trustworthy decision-making solutions.

Language: Английский

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Deep Learning in Automatic Diabetic Retinopathy Detection and Grading Systems: A Comprehensive Survey and Comparison of Methods

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 84785 - 84802

Published: Jan. 1, 2024

Language: Английский

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A novel fusion approach with a robust ParallelNet model for diabetic retinopathy diagnosis

Pattern Analysis and Applications, Journal Year: 2025, Volume and Issue: 28(2)

Published: March 21, 2025

Language: Английский

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Optimized Machine Learning for the Early Detection of Polycystic Ovary Syndrome in Women

Sensors, Journal Year: 2025, Volume and Issue: 25(4), P. 1166 - 1166

Published: Feb. 14, 2025

Polycystic ovary syndrome (PCOS) is a medical condition that impacts millions of women worldwide; however, due to lack public awareness, as well the expensive testing involved in identification PCOS, 70% cases go undiagnosed. Therefore, primary objective this study design an expert machine learning (ML) model for early diagnosis PCOS based on initial symptoms and health indicators; two datasets were amalgamated preprocessed accomplish goal, resulting new symptomatic dataset with 12 attributes. An ensemble (EL) model, seven base classifiers, deep (DL) meta-level classifier, are proposed. The hyperparameters EL optimized through nature-inspired walrus optimization (WaO), cuckoo search (CSO), random (RSO) algorithms, leading WaOEL, CSOEL, RSOEL models, respectively. results obtained prove supremacy designed WaOEL over other prediction accuracy 92.8% area under receiver operating characteristic curve (AUC) 0.93; moreover, feature importance analysis, presented forest (RF) Shapley additive values (SHAP) positive predictions, highlights crucial clinical insights need intervention. Our findings suggest patients features related obesity high cholesterol more likely be diagnosed positive. Most importantly, it inferred from without tests possible proposed which helps clinicians make better informed decisions, identify comorbidities, reduce harmful long-term effects PCOS.

Language: Английский

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Diabetic retinopathy detection and classification using DENSENET-121

AIP conference proceedings, Journal Year: 2025, Volume and Issue: 3258, P. 020011 - 020011

Published: Jan. 1, 2025

Language: Английский

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Segmented Fractal and Central Symmetric LBP Based Texture Features for the Detection of Diabetic Retinopathy Using SVM

SN Computer Science, Journal Year: 2024, Volume and Issue: 5(5)

Published: June 13, 2024

Language: Английский

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Application of Artificial Intelligence for Classification, Segmentation, Early Detection, Early Diagnosis, and Grading of Diabetic Retinopathy from Fundus Retinal Images: A Comprehensive Review

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 172499 - 172536

Published: Jan. 1, 2024

Language: Английский

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SDRG-Net: Secure DR grading network for Real-Time decision support in IoMT environments

Biomedical Signal Processing and Control, Journal Year: 2024, Volume and Issue: 100, P. 107040 - 107040

Published: Oct. 7, 2024

Language: Английский

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