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Application of Optical Coherence Tomography to a Mouse Model of Retinopathy
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Enhanced Deep Learning Model for Classification of Retinal Optical Coherence Tomography Images.

Esraa Hassan1, Samir Elmougy2, Mai R Ibraheem3

  • 1Faculty of Artificial Intelligence, Kafrelsheikh University, Kafrelsheikh 33516, Egypt.

Sensors (Basel, Switzerland)
|July 8, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces an enhanced optical coherence tomography (EOCT) model for accurate retinal image classification. The EOCT model significantly improves diagnostic precision for eye conditions using deep learning algorithms.

Keywords:
artificial intelligencedeep learningoptical coherence tomography (OCT)optical sensor technologies

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Retinal optical coherence tomography (OCT) is crucial for diagnosing eye conditions and monitoring systemic diseases like diabetes.
  • Accurate diagnosis and automated analysis of OCT images are essential for effective clinical practice.
  • Existing models require enhancement for improved specificity and performance in retinal image classification.

Purpose of the Study:

  • To propose an enhanced optical coherence tomography (EOCT) model for classifying retinal OCT images.
  • To improve the performance of automated retinal image analysis using deep learning.
  • To achieve higher accuracy in diagnosing various retinal conditions.

Main Methods:

  • Development of an enhanced optical coherence tomography (EOCT) model.
  • Utilizing a modified ResNet (50) architecture combined with random forest algorithms for classification.
  • Employing the Adam optimizer to enhance the efficiency of the ResNet (50) model during training.

Main Results:

  • The EOCT model achieved high performance metrics, including sensitivity (0.9836), specificity (0.9615), and accuracy (0.9747).
  • The model demonstrated superior efficiency compared to pre-trained models like VGG (16).
  • Key performance indicators such as precision and Matthew's correlation coefficient were also notably high, indicating robust classification capabilities.

Conclusions:

  • The proposed EOCT model offers a significant advancement in automated retinal OCT image classification.
  • This enhanced model holds promise for improving diagnostic accuracy and patient outcomes in ophthalmology.
  • Further research can explore the integration of EOCT in clinical workflows for real-time disease detection.