Frobenius deep feature fusion architecture to detect diabetic retinopathy
Abstract Purpose Diabetic retinopathy is a medical complication affecting the retina of patients prone to prolonged periods of Diabetes mellitus. Early detection and diagnosis are essential since the symptoms are subtle at the early stages. It can result in permanent vision impairment at advanced st...
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| Format: | Article |
| Language: | English |
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Springer
2025-03-01
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| Series: | Discover Applied Sciences |
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| Online Access: | https://doi.org/10.1007/s42452-025-06575-4 |
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| author | C. Priyadharsini Y. Asnath Victy Phamila |
| author_facet | C. Priyadharsini Y. Asnath Victy Phamila |
| author_sort | C. Priyadharsini |
| collection | DOAJ |
| description | Abstract Purpose Diabetic retinopathy is a medical complication affecting the retina of patients prone to prolonged periods of Diabetes mellitus. Early detection and diagnosis are essential since the symptoms are subtle at the early stages. It can result in permanent vision impairment at advanced stages if not managed properly. Detecting various severity levels helps identify the level of treatment required for each patient, and directing them to the appropriate intensive care unit helps optimize the overall treatment efficacy. Methods This work proposes a multi-model architecture by combining the features extracted from convolutional neural networks using novel Frobenius norm-based feature fusion with an ensemble of machine learning classifiers to perform the classification of binary and multi-class stages of Diabetic Retinopathy. The proposed approach delves into various phases- data collection and data pre-processing, feature extraction from VGG16 and Densenet201, feature selection using Random Forest, feature fusion using Frobenius norm, and classification using stacked ensembling of XGBoost classifier and ExtraTreeClassifier with SVC as meta-learner. Results The architecture is trained, and performance is assessed with the test set of the APTOS dataset, using accuracy, F1-score, precision, and recall metrics. The proposed architecture achieved the best accuracy of 98 percent for binary classification and 80.63 percent for multi-class classification. Conclusion The performance of the recommended Frobenius-based deep feature fusion architecture achieved the highest accuracy for binary and multiclass classification compared to other related research works. |
| format | Article |
| id | doaj-art-2cfbe0a5e1314fc98a35b24638c2bb5a |
| institution | DOAJ |
| issn | 3004-9261 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Applied Sciences |
| spelling | doaj-art-2cfbe0a5e1314fc98a35b24638c2bb5a2025-08-20T02:59:54ZengSpringerDiscover Applied Sciences3004-92612025-03-017311110.1007/s42452-025-06575-4Frobenius deep feature fusion architecture to detect diabetic retinopathyC. Priyadharsini0Y. Asnath Victy Phamila1School of Computer Science and Engineering, Vellore Institute of TechnologySchool of Computer Science and Engineering, Vellore Institute of TechnologyAbstract Purpose Diabetic retinopathy is a medical complication affecting the retina of patients prone to prolonged periods of Diabetes mellitus. Early detection and diagnosis are essential since the symptoms are subtle at the early stages. It can result in permanent vision impairment at advanced stages if not managed properly. Detecting various severity levels helps identify the level of treatment required for each patient, and directing them to the appropriate intensive care unit helps optimize the overall treatment efficacy. Methods This work proposes a multi-model architecture by combining the features extracted from convolutional neural networks using novel Frobenius norm-based feature fusion with an ensemble of machine learning classifiers to perform the classification of binary and multi-class stages of Diabetic Retinopathy. The proposed approach delves into various phases- data collection and data pre-processing, feature extraction from VGG16 and Densenet201, feature selection using Random Forest, feature fusion using Frobenius norm, and classification using stacked ensembling of XGBoost classifier and ExtraTreeClassifier with SVC as meta-learner. Results The architecture is trained, and performance is assessed with the test set of the APTOS dataset, using accuracy, F1-score, precision, and recall metrics. The proposed architecture achieved the best accuracy of 98 percent for binary classification and 80.63 percent for multi-class classification. Conclusion The performance of the recommended Frobenius-based deep feature fusion architecture achieved the highest accuracy for binary and multiclass classification compared to other related research works.https://doi.org/10.1007/s42452-025-06575-4Diabetic retinopathy multiclass classificationConvolutional neural networkFeature engineeringFrobenius normFeature selectionEnsemble learning |
| spellingShingle | C. Priyadharsini Y. Asnath Victy Phamila Frobenius deep feature fusion architecture to detect diabetic retinopathy Discover Applied Sciences Diabetic retinopathy multiclass classification Convolutional neural network Feature engineering Frobenius norm Feature selection Ensemble learning |
| title | Frobenius deep feature fusion architecture to detect diabetic retinopathy |
| title_full | Frobenius deep feature fusion architecture to detect diabetic retinopathy |
| title_fullStr | Frobenius deep feature fusion architecture to detect diabetic retinopathy |
| title_full_unstemmed | Frobenius deep feature fusion architecture to detect diabetic retinopathy |
| title_short | Frobenius deep feature fusion architecture to detect diabetic retinopathy |
| title_sort | frobenius deep feature fusion architecture to detect diabetic retinopathy |
| topic | Diabetic retinopathy multiclass classification Convolutional neural network Feature engineering Frobenius norm Feature selection Ensemble learning |
| url | https://doi.org/10.1007/s42452-025-06575-4 |
| work_keys_str_mv | AT cpriyadharsini frobeniusdeepfeaturefusionarchitecturetodetectdiabeticretinopathy AT yasnathvictyphamila frobeniusdeepfeaturefusionarchitecturetodetectdiabeticretinopathy |