Diabetic retinopathy disease detection using shapley additive ensembled densenet-121 resnet-50 model

Mary, A. Rosline; Kavitha, P.

doi:10.1007/s11042-024-18309-6

Multimed Tools Appl

2024

DOI: 10.1007/s11042-024-18309-6

|View full text |Cite

Diabetic retinopathy disease detection using shapley additive ensembled densenet-121 resnet-50 model

A. Rosline Mary,

P. Kavitha

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article5

Relationship

Self Cite0

Independent5

Authors

Journals

Cited by 5 publications

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Revolutionizing Climbing Perch Disease Management: AI-Driven Solutions for Sustainable Aquaculture

Sriputhorn,

Pitakaso,

Matitopanum

et al. 2024

Smart Agricultural Technology

View full text Add to dashboard Cite

Revolutionizing Climbing Perch Disease Management: AI-Driven Solutions for Sustainable Aquaculture

Sriputhorn,

Pitakaso,

Matitopanum

et al. 2024

Smart Agricultural Technology

View full text Add to dashboard Cite

Integrated ensemble CNN and explainable AI for COVID-19 diagnosis from CT scan and X-ray images

Rajpoot,

Gour,

Jain

et al. 2024

Sci Rep

View full text Add to dashboard Cite

In light of the ongoing battle against COVID-19, while the pandemic may eventually subside, sporadic cases may still emerge, underscoring the need for accurate detection from radiological images. However, the limited explainability of current deep learning models restricts clinician acceptance. To address this issue, our research integrates multiple CNN models with explainable AI techniques, ensuring model interpretability before ensemble construction. Our approach enhances both accuracy and interpretability by evaluating advanced CNN models on the largest publicly available X-ray dataset, COVIDx CXR-3, which includes 29,986 images, and the CT scan dataset for SARS-CoV-2 from Kaggle, which includes a total of 2,482 images. We also employed additional public datasets for cross-dataset evaluation, ensuring a thorough assessment of model performance across various imaging conditions. By leveraging methods including LIME, SHAP, Grad-CAM, and Grad-CAM++, we provide transparent insights into model decisions. Our ensemble model, which includes DenseNet169, ResNet50, and VGG16, demonstrates strong performance. For the X-ray image dataset, sensitivity, specificity, accuracy, F1-score, and AUC are recorded at 99.00%, 99.00%, 99.00%, 0.99, and 0.99, respectively. For the CT image dataset, these metrics are 96.18%, 96.18%, 96.18%, 0.9618, and 0.96, respectively. Our methodology bridges the gap between precision and interpretability in clinical settings by combining model diversity with explainability, promising enhanced disease diagnosis and greater clinician acceptance.

show abstract

Deep Learning in Automatic Diabetic Retinopathy Detection and Grading Systems: A Comprehensive Survey and Comparison of Methods

Abushawish,

Modak,

Abdel-Raheem

et al. 2024

IEEE Access

View full text Add to dashboard Cite

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Diabetic retinopathy disease detection using shapley additive ensembled densenet-121 resnet-50 model

Cited by 5 publications

References 37 publications

Revolutionizing Climbing Perch Disease Management: AI-Driven Solutions for Sustainable Aquaculture

Revolutionizing Climbing Perch Disease Management: AI-Driven Solutions for Sustainable Aquaculture

Integrated ensemble CNN and explainable AI for COVID-19 diagnosis from CT scan and X-ray images

Deep Learning in Automatic Diabetic Retinopathy Detection and Grading Systems: A Comprehensive Survey and Comparison of Methods

Contact Info

Product

Resources

About