Using SHAP Analysis to Detect Areas Contributing to Diabetic Retinopathy Detection
Author:
Affiliation:
1. Schulich School of Engineering, University of Calgary,Department of Electrical and Software Engineering,Canada
2. Cumming School of Medicine, University of Calgary,Canada
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9873849/9873997/09874222.pdf?arnumber=9874222
Reference26 articles.
1. A Deep Learning Ensemble Approach for Diabetic Retinopathy Detection
2. Deep Transfer Learning Models for Medical Diabetic Retinopathy Detection
3. Comparative analysis of deep learning methods of detection of diabetic retinopathy
4. Diabetic Retinopathy Detection Using Transfer Learning and Deep Learning
5. Deep Learning Approach to Diabetic Retinopathy Detection
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1. Explaining Deep Learning Decisions Via Fuzzy Inference System on Medical Images;2023 IEEE 21st Student Conference on Research and Development (SCOReD);2023-12-13
2. Deep Learning-Based Detection and Classification of Uveal Melanoma Using Convolutional Neural Networks and SHAP Analysis;2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI);2023-08
3. Machine learning application for predicting smoking cessation among US adults: An analysis of waves 1-3 of the PATH study;PLOS ONE;2023-06-08
4. Explaining Eye Diseases Detected by Machine Learning Using SHAP: A Case Study of Diabetic Retinopathy and Choroidal Nevus;SN Computer Science;2023-06-07
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