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Deep Learning Techniques for Diabetic Retinopathy Detection.

Sehrish Qummar¹, Fiaz Gul Khan¹, Sajid Shah¹

¹Department of Computer Science, COMSATS University Islamabad, Abbottabad Campus, Abbottabad, Pakistan.

Current Medical Imaging

|February 29, 2020

Summary

This summary is machine-generated.

This study reviews machine and deep learning methods for automatic Diabetic Retinopathy (DR) detection. It analyzes techniques, datasets, and future directions for identifying DR, a complication of diabetes.

Keywords:

Diabetic retinopathy convolutional Neural Network deep learning diabetes lesions detection machine learning

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Area of Science:

Ophthalmology
Medical Imaging
Artificial Intelligence

Background:

Diabetes mellitus can lead to Diabetic Retinopathy (DR), a serious complication affecting retinal blood vessels.
Manual DR detection by ophthalmologists is labor-intensive and time-consuming.
Automated DR detection methods are crucial for timely diagnosis and treatment.

Purpose of the Study:

To conduct a comprehensive review of machine and deep learning techniques for DR identification and classification.
To evaluate the strengths and weaknesses of various datasets used in DR detection methods.
To outline future research directions in automated DR detection.

Main Methods:

Literature review of existing studies on DR detection using AI.
Analysis of different AI techniques, including machine learning and deep learning.
Examination of dataset characteristics and their impact on DR detection performance.

Main Results:

Overview of various automated DR detection and classification techniques.
Discussion on the advantages and limitations of commonly used datasets for DR analysis.
Identification of key steps in DR detection: retinal blood vessel segmentation, lesion detection, and abnormality identification.

Conclusions:

Machine and deep learning offer promising avenues for automated DR detection.
Further research is needed to address dataset limitations and enhance detection accuracy.
Standardized methodologies and robust datasets are essential for advancing automated DR screening.