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Using Retinal Imaging to Study Dementia

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HDR-EfficientNet: A Classification of Hypertensive and Diabetic Retinopathy Using Optimize EfficientNet Architecture.

Qaisar Abbas¹, Yassine Daadaa¹, Umer Rashid²

¹College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia.

Diagnostics (Basel, Switzerland)

|October 28, 2023

Summary

This summary is machine-generated.

A new deep learning method, HDR-EfficientNet, efficiently detects hypertensive retinopathy (HR) and diabetic retinopathy (DR) using retinal images. This advanced classifier shows high accuracy, aiding in early diagnosis and blindness prevention.

Keywords:

convolutional neural network deep learning diabetic retinopathy hypertensive retinopathy inception model transfer learning

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Area of Science:

Ophthalmology
Medical Imaging
Artificial Intelligence

Background:

Hypertensive retinopathy (HR) and diabetic retinopathy (DR) are leading causes of blindness, linked to hypertension and diabetes.
Current computer-aided detection (CAD) methods for HR and DR often use traditional machine learning, requiring complex image processing and offering limited applications.
Early identification and assessment of HR are critical for preventing vision loss.

Purpose of the Study:

To introduce HDR-EfficientNet, a deep learning (DL) model for efficient and accurate detection of hypertensive retinopathy (HR) and diabetic retinopathy (DR).
To leverage an EfficientNet-V2 architecture with spatial-channel attention and transfer learning for improved classification performance.
To enhance feature selection capacity through the integration of dense layers.

Main Methods:

Developed HDR-EfficientNet using an EfficientNet-V2 backbone for end-to-end disease classification.
Incorporated a spatial-channel attention mechanism to pinpoint retinal damage and differentiate between conditions.
Utilized transfer learning to address imbalanced datasets and improve model generalization, augmented with dense layers for feature selection.

Main Results:

Evaluated on over 36,000 augmented retinal fundus images, achieving high performance metrics.
Demonstrated an average area under the curve (AUC) of 0.98.
Reported specificity (SP) of 96%, accuracy (ACC) of 98%, and sensitivity (SE) of 95% for HR and DR detection.

Conclusions:

The HDR-EfficientNet model presents a highly accurate and efficient deep learning-based approach for diagnosing hypertensive retinopathy and diabetic retinopathy.
The method effectively classifies HR and DR, offering significant support for clinical diagnosis and management.
This DL approach overcomes limitations of traditional methods, paving the way for improved eye care.