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

Area of Science:

Medical image analysis
Artificial intelligence in healthcare
Ophthalmology

Background:

Deep learning models offer potential in medical image analysis but lack transparency, hindering trust in high-stakes healthcare decisions.
Uncertainty quantification is critical for clinical decision-making, especially in conditions like diabetic retinopathy where errors can have severe consequences.
Traditional deep learning models provide single-point predictions, failing to capture essential uncertainty measures.

Purpose of the Study:

To implement and evaluate Bayesian extensions to a DenseNet-121 model for improved diabetic retinopathy detection.
To assess the effectiveness of different Bayesian approximation techniques (Monte Carlo Dropout, Mean Field Variational Inference, Deterministic Inference) in quantifying prediction uncertainty.
To enhance the trustworthiness of deep learning models in medical image analysis through uncertainty estimation.

Main Methods:

Transfer learning using the DenseNet-121 convolutional neural network architecture.
Application of Bayesian approximation techniques: Monte Carlo Dropout, Mean Field Variational Inference, and Deterministic Inference.
Experiments conducted on a combined dataset (APTOS 2019 + DDR) with pre-processed images.

Main Results:

Bayesian-augmented DenseNet-121 models demonstrated superior performance compared to state-of-the-art methods.
Achieved high test accuracies: 97.68% (Monte Carlo Dropout), 94.23% (Mean Field Variational Inference), and 91.44% (Deterministic).
Uncertainty was quantified using entropy and standard deviation metrics, providing insights into model confidence.

Conclusions:

Bayesian deep learning significantly improves classification accuracy for diabetic retinopathy detection.
Uncertainty estimation derived from Bayesian methods enhances the reliability and trustworthiness of AI-driven clinical decision support systems.
The integration of uncertainty quantification is vital for the safe and effective deployment of deep learning in healthcare.