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dcnn (alexnet)  (Johns Hopkins HealthCare)

 
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    Structured Review

    Johns Hopkins HealthCare dcnn (alexnet)
    Concise introduction of CNN algorithms used in AI diagnosis.
    Dcnn (Alexnet), supplied by Johns Hopkins HealthCare, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/dcnn+(alexnet)/dcnn++alexnet+/pmc06276430-65-26-10
    Average 90 stars, based on 1 article reviews
    dcnn (alexnet) - by Bioz Stars, 2026-10
    90/100 stars

    Images

    1) Product Images from "Applications of Artificial Intelligence in Ophthalmology: General Overview"

    Article Title: Applications of Artificial Intelligence in Ophthalmology: General Overview

    Journal: Journal of Ophthalmology

    doi: 10.1155/2018/5278196

    Concise introduction of CNN algorithms used in AI diagnosis.
    Figure Legend Snippet: Concise introduction of CNN algorithms used in AI diagnosis.

    Techniques Used: Biomarker Discovery

    Studies on eye diseases using DL techniques.
    Figure Legend Snippet: Studies on eye diseases using DL techniques.

    Techniques Used: Injection, Medications, Biomarker Discovery

    Related Articles

    Biomarker Discovery:

    Article Title: Applications of Artificial Intelligence in Ophthalmology: General Overview
    Article Snippet: Burlina et al. [ ] (Retina Division, Wilmer Eye Institute, Johns Hopkins University School of Medicine) , AMD detection , Public: AREDS 130000 fundus images , DCNN (AlexNet) , AUC: 0.94∼0.96 Accuracy: 88.4%∼91.6% , Applying a DL-based automated assessment of AMD from fundus images can produce results that are similar to human performance levels.

    Injection:

    Article Title: Applications of Artificial Intelligence in Ophthalmology: General Overview
    Article Snippet: Burlina et al. [ ] (Retina Division, Wilmer Eye Institute, Johns Hopkins University School of Medicine) , AMD detection , Public: AREDS 130000 fundus images , DCNN (AlexNet) , AUC: 0.94∼0.96 Accuracy: 88.4%∼91.6% , Applying a DL-based automated assessment of AMD from fundus images can produce results that are similar to human performance levels.

    Medications:

    Article Title: Applications of Artificial Intelligence in Ophthalmology: General Overview
    Article Snippet: Burlina et al. [ ] (Retina Division, Wilmer Eye Institute, Johns Hopkins University School of Medicine) , AMD detection , Public: AREDS 130000 fundus images , DCNN (AlexNet) , AUC: 0.94∼0.96 Accuracy: 88.4%∼91.6% , Applying a DL-based automated assessment of AMD from fundus images can produce results that are similar to human performance levels.



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    Image Search Results


    Classification-based studies in DR detection using fundus imaging.

    Journal: Journal of Imaging

    Article Title: Automated Detection and Diagnosis of Diabetic Retinopathy: A Comprehensive Survey

    doi: 10.3390/jimaging7090165

    Figure Lengend Snippet: Classification-based studies in DR detection using fundus imaging.

    Article Snippet: Lam, 2018 [ ] , EyePACS, eOphta , Ex, MA, HE, NV , NA , DCNN: AlexNet, VGG16, GoogLeNet, ResNet, and Inception-v3 , NA/NA , EyePACS: 0.99 ACC: 98.0%.

    Techniques: Imaging, Modification, Extraction

    Classification-based studies in DR detection using a special preprocessing on fundus images for DR grading in ICDR scale.

    Journal: Journal of Imaging

    Article Title: Automated Detection and Diagnosis of Diabetic Retinopathy: A Comprehensive Survey

    doi: 10.3390/jimaging7090165

    Figure Lengend Snippet: Classification-based studies in DR detection using a special preprocessing on fundus images for DR grading in ICDR scale.

    Article Snippet: Lam, 2018 [ ] , EyePACS, eOphta , Ex, MA, HE, NV , NA , DCNN: AlexNet, VGG16, GoogLeNet, ResNet, and Inception-v3 , NA/NA , EyePACS: 0.99 ACC: 98.0%.

    Techniques:

    Classification-based studies in DR detection using OCT and OCTA.

    Journal: Journal of Imaging

    Article Title: Automated Detection and Diagnosis of Diabetic Retinopathy: A Comprehensive Survey

    doi: 10.3390/jimaging7090165

    Figure Lengend Snippet: Classification-based studies in DR detection using OCT and OCTA.

    Article Snippet: Lam, 2018 [ ] , EyePACS, eOphta , Ex, MA, HE, NV , NA , DCNN: AlexNet, VGG16, GoogLeNet, ResNet, and Inception-v3 , NA/NA , EyePACS: 0.99 ACC: 98.0%.

    Techniques: Extraction

    Segmentation-based studies in DR detection using fundus images.

    Journal: Journal of Imaging

    Article Title: Automated Detection and Diagnosis of Diabetic Retinopathy: A Comprehensive Survey

    doi: 10.3390/jimaging7090165

    Figure Lengend Snippet: Segmentation-based studies in DR detection using fundus images.

    Article Snippet: Lam, 2018 [ ] , EyePACS, eOphta , Ex, MA, HE, NV , NA , DCNN: AlexNet, VGG16, GoogLeNet, ResNet, and Inception-v3 , NA/NA , EyePACS: 0.99 ACC: 98.0%.

    Techniques: Extraction

    Segmentation-based studies in DR detection using OCT, OCTA images.

    Journal: Journal of Imaging

    Article Title: Automated Detection and Diagnosis of Diabetic Retinopathy: A Comprehensive Survey

    doi: 10.3390/jimaging7090165

    Figure Lengend Snippet: Segmentation-based studies in DR detection using OCT, OCTA images.

    Article Snippet: Lam, 2018 [ ] , EyePACS, eOphta , Ex, MA, HE, NV , NA , DCNN: AlexNet, VGG16, GoogLeNet, ResNet, and Inception-v3 , NA/NA , EyePACS: 0.99 ACC: 98.0%.

    Techniques: Control, Extraction

    Concise introduction of CNN algorithms used in AI diagnosis.

    Journal: Journal of Ophthalmology

    Article Title: Applications of Artificial Intelligence in Ophthalmology: General Overview

    doi: 10.1155/2018/5278196

    Figure Lengend Snippet: Concise introduction of CNN algorithms used in AI diagnosis.

    Article Snippet: Burlina et al. [ ] (Retina Division, Wilmer Eye Institute, Johns Hopkins University School of Medicine) , AMD detection , Public: AREDS 130000 fundus images , DCNN (AlexNet) , AUC: 0.94∼0.96 Accuracy: 88.4%∼91.6% , Applying a DL-based automated assessment of AMD from fundus images can produce results that are similar to human performance levels.

    Techniques: Biomarker Discovery

    Studies on eye diseases using DL techniques.

    Journal: Journal of Ophthalmology

    Article Title: Applications of Artificial Intelligence in Ophthalmology: General Overview

    doi: 10.1155/2018/5278196

    Figure Lengend Snippet: Studies on eye diseases using DL techniques.

    Article Snippet: Burlina et al. [ ] (Retina Division, Wilmer Eye Institute, Johns Hopkins University School of Medicine) , AMD detection , Public: AREDS 130000 fundus images , DCNN (AlexNet) , AUC: 0.94∼0.96 Accuracy: 88.4%∼91.6% , Applying a DL-based automated assessment of AMD from fundus images can produce results that are similar to human performance levels.

    Techniques: Injection, Medications, Biomarker Discovery