An Interpretable Ensemble Deep Learning Model for Diabetic Retinopathy Disease Classification
Diabetic retinopathy (DR) is one kind of eye disease that is caused by overtime diabetes. Lots of patients around the world suffered from DR which may bring about blindness. Early detection of DR is a rigid quest which can remind the DR patients to seek corresponding treatments in time. This paper presents an automatic image-level DR detection system using multiple well-trained deep learning models. Besides, several deep learning models are integrated using the Adaboost algorithm in order to reduce the bias of each single model. To explain the results of DR detection, this paper provides weighted class activation maps (CAMs) that can illustrate the suspected position of lesions. In the pre-processing stage, eight image transformation ways are also introduced to help augment the diversity of fundus images. Experiments demonstrate that the method proposed by this paper has stronger robustness and acquires more excellent performance than that of individual deep learning model.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2019 |
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| Erschienen: |
2019 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:2019 |
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| Enthalten in: |
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference - 2019(2019) vom: 15. Juli, Seite 2045-2048 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Jiang, Hongyang [VerfasserIn] |
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| Anmerkungen: |
Date Completed 23.04.2020 Date Revised 28.09.2020 published: Print Citation Status MEDLINE |
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| doi: |
10.1109/EMBC.2019.8857160 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM305446061 |
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| 245 | 1 | 3 | |a An Interpretable Ensemble Deep Learning Model for Diabetic Retinopathy Disease Classification |
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| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Diabetic retinopathy (DR) is one kind of eye disease that is caused by overtime diabetes. Lots of patients around the world suffered from DR which may bring about blindness. Early detection of DR is a rigid quest which can remind the DR patients to seek corresponding treatments in time. This paper presents an automatic image-level DR detection system using multiple well-trained deep learning models. Besides, several deep learning models are integrated using the Adaboost algorithm in order to reduce the bias of each single model. To explain the results of DR detection, this paper provides weighted class activation maps (CAMs) that can illustrate the suspected position of lesions. In the pre-processing stage, eight image transformation ways are also introduced to help augment the diversity of fundus images. Experiments demonstrate that the method proposed by this paper has stronger robustness and acquires more excellent performance than that of individual deep learning model | ||
| 650 | 4 | |a Journal Article | |
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| 700 | 1 | |a Yang, Kang |e verfasserin |4 aut | |
| 700 | 1 | |a Gao, Mengdi |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Dongdong |e verfasserin |4 aut | |
| 700 | 1 | |a Ma, He |e verfasserin |4 aut | |
| 700 | 1 | |a Qian, Wei |e verfasserin |4 aut | |
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