Deep learning based hemorrhages classification using dcnn with optimized LSTM
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Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s11042-023-18022-w.pdf
Reference23 articles.
1. Hacisoftaoglu RE, Karakaya M, Sallam AB (2020) Deep learning frameworks for diabetic retinopathy detection with smartphone-based retinal imaging systems. Pattern Recogn Lett 135:409–417. https://doi.org/10.1016/j.patrec.2020.04.009
2. Saxena G, Verma DK, Paraye A, Rajan A, Rawat A (2020) Improved and robust deep learning agent for preliminary detection of diabetic retinopathy using public datasets. Intelligence-Based Medicine 3:100022. https://doi.org/10.1016/j.ibmed.2020.100022
3. Lahiri S (2020) Hybrid deep learning convolutional neural networks and optimal nonlinear support vector machine to detect presence of hemorrhage in retina. Biomed Signal Process Control 60:101978. https://doi.org/10.1016/j.bspc.2020.101978
4. Gangwar AK, Ravi V (2021) Diabetic retinopathy detection using transfer learning and deep learning. In Evolution in Computational Intelligence 1(679–689). Springer, Singapore. https://doi.org/10.1007/978-981-15-5788-0_64
5. Zhang D, Bu W, Wu X (2017) Diabetic retinopathy classification using deeply supervised ResNet. In 2017 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computed, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People, and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI) (pp 1–6). IEEE. https://doi.org/10.1109/UIC-ATC.2017.8397469
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