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A comparison of reported deep learning techniques for screening and recognition.

Journal: Sensors (Basel, Switzerland)

Article Title: Deep Learning for Diabetic Retinopathy Analysis: A Review, Research Challenges, and Future Directions

doi: 10.3390/s22186780

Figure Lengend Snippet: A comparison of reported deep learning techniques for screening and recognition.

Article Snippet: Silvia Rego et al. [ ] , CNN model with Inception-V3 based software , Not Mentioned , EyePACS , 350 , Color fundus images , Sensitivity = 80.8% Specificity = 95.6% PPV = 77.6% NPV = 96.3%.

Techniques: Comparison, Software

Summary of reported techniques for lesion detection.

Journal: Sensors (Basel, Switzerland)

Article Title: Deep Learning for Diabetic Retinopathy Analysis: A Review, Research Challenges, and Future Directions

doi: 10.3390/s22186780

Figure Lengend Snippet: Summary of reported techniques for lesion detection.

Article Snippet: Silvia Rego et al. [ ] , CNN model with Inception-V3 based software , Not Mentioned , EyePACS , 350 , Color fundus images , Sensitivity = 80.8% Specificity = 95.6% PPV = 77.6% NPV = 96.3%.

Techniques: Software, Plasmid Preparation, Biomarker Discovery, Modification, Tomography, Blocking Assay

Performances of existing DR detection methods

Journal: Health Information Science and Systems

Article Title: Features extraction using encoded local binary pattern for detection and grading diabetic retinopathy

doi: 10.1007/s13755-022-00181-z

Figure Lengend Snippet: Performances of existing DR detection methods

Article Snippet: Sengupta et al. [ ] , EyePACS , Inception-v3 Model , Acc: 90. 4%, Sensitivity: 90%.

Techniques: Software