The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.
Fundus Image Classification Using VGG-19 Architecture with PCA and SVD
Open accessAbstract
Automated medical image analysis is an emerging field of research that identifies the disease with the help of imaging technology. Diabetic retinopathy (DR) is a retinal disease that is diagnosed in diabetic patients. Deep neural network (DNN) is widely used to classify diabetic retinopathy from fundus images collected from suspected persons. The proposed DR classification system achieves a symmetrically optimized solution through the combination of a Gaussian mixture model (GMM), visual geometry group network (VGGNet), singular value decomposition (SVD) and principle component analysis (PCA), and softmax, for region segmentation, high dimensional feature extraction, feature selection and fundus image classification, respectively. The experiments were performed using a standard KAGGLE dataset containing 35,126 images. The proposed VGG-19 DNN based DR model outperformed the AlexNet and spatial invariant feature transform (SIFT) in terms of classification accuracy and computational time. Utilization of PCA and SVD feature selection with fully connected (FC) layers demonstrated the classification accuracies of 92.21%, 98.34%, 97.96%, and 98.13% for FC7-PCA, FC7-SVD, FC8-PCA, and FC8-SVD, respectively.
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A survey on deep learning in medical image analysis
Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., van der Laak, J. A. W. M., van Ginneken, B., & Sánchez, C. I. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.07.005 · Cited by 15,332
Gulshan, V., Peng, L., Coram, M., Stumpe, M. C., Wu, D., Narayanaswamy, A., Venugopalan, S., Widner, K., Madams, T., Cuadros, J., et al. (2016) · JAMA · doi:10.1001/jama.2016.17216 · Cited by 7,697
Adaptive background mixture models for real-time tracking
Stauffer, C., & Grimson, W. E. L. (2003) · Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149) · doi:10.1109/cvpr.1999.784637 · Cited by 6,923
The random subspace method for constructing decision forests
Ho, T. K. (1998) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.709601 · Cited by 6,842
Recent advances in convolutional neural networks
Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2017.10.013 · Cited by 6,576
Convolutional Neural Networks for Diabetic Retinopathy
Pratt, H., Coenen, F., Broadbent, D. M., Harding, S. P., & Zheng, Y. (2016) · Procedia Computer Science · doi:10.1016/j.procs.2016.07.014 · Cited by 988
the DIARETDB1 diabetic retinopathy database and evaluation protocol
Kauppi, T., Kalesnykiene, V., Kamarainen, J.-K., Lensu, L., Sorri, I., Raninen, A., Voutilainen, R., Uusitalo, H., Kalviainen, H., & Pietila, J. (2007) · Procedings of the British Machine Vision Conference 2007 · doi:10.5244/c.21.15 · Cited by 854
Handcrafted vs. non-handcrafted features for computer vision classification
Nanni, L., Ghidoni, S., & Brahnam, S. (2017) · Pattern Recognition · doi:10.1016/j.patcog.2017.05.025 · Cited by 542 · cited 1×
“[37] designed a computer vision system based on non-handcrafted and handcrafted features.”
BackgroundDeep image mining for diabetic retinopathy screening
Quellec, G., Charrière, K., Boudi, Y., Cochener, B., & Lamard, M. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.04.012 · Cited by 520
van Grinsven, M. J. J. P., van Ginneken, B., Hoyng, C. B., Theelen, T., & Sanchez, C. I. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2526689 · Cited by 426
Cited by
A Survey of Deep Active Learning
Ren, P., Xiao, Y., Chang, X., Huang, P.-Y., Li, Z., Gupta, B. B., Chen, X., & Wang, X. (2022) · ACM Computing Surveys · doi:10.1145/3472291 · Cited by 1,169
Fake online reviews: Literature review, synthesis, and directions for future research
Wu, Y., Ngai, E. W. T., Wu, P., & Wu, C. (2020) · Decision Support Systems · doi:10.1016/j.dss.2020.113280 · Cited by 359
Deep Learning-Based Transfer Learning for Classification of Skin Cancer
Jain, S., Singhania, U., Tripathy, B., Nasr, E. A., Aboudaif, M. K., & Kamrani, A. K. (2021) · Sensors · doi:10.3390/s21238142 · Cited by 169
Image captioning by diffusion models: a survey
Daneshfar, F., Bartani, A., & Lotfi, P. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.109288 · Cited by 49
Technological Interventions for Pain Management
Suso-Ribera, C., Castilla, D., Martínez-Borba, V., Jaén, I., Botella, C., Baños, R. M., & García-Palacios, A. (2022) · Comprehensive Clinical Psychology · doi:10.1016/b978-0-12-818697-8.00009-1 · Cited by 6
Metadata
- Authors
- Mateen, M., Wen, J., Nasrullah, Song, S., & Huang, Z.
- Venue
- Symmetry
- Publisher
- MDPI AG
- Published
- 2018-12-20
- Volume
- 11
- Issue
- 1
- Pages
- 1
- ISSN
- 2073-8994 (ISSN-L)
- OpenAlex
- W2906424845
- Cited by
- 614
- References
- 30
- FWCI
- 51.19
- Type
- journal article
- Language
- en
- Open access
- gold
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