PREDIKSI PENYAKIT MATA MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK
Abstract
Full Text:
PDFReferences
X. Xu, L. Zhang, J. Li, Y. Guan and L. Zhang, "A Hybrid Global-Local Representation CNN Model for Automatic Cataract Grading," in IEEE Journal of Biomedical and Health Informatics, vol. 24, no. 2, pp. 556-567, Feb. 2020, doi: 10.1109/JBHI.2019.2914690.
Ren, L., Dong, J., Wang, X., Meng, Z., Zhao, L., & Deen, M. J. (2021). A Data-Driven Auto-CNN-LSTM Prediction Model for Lithium-Ion Battery Remaining Useful Life. IEEE Transactions on Industrial Informatics, 17(5), 3478–3487. https://doi.org/10.1109/TII.2020.3008223
Mzoughi, H., Njeh, I., Wali, A., Slima, M. ben, BenHamida, A., Mhiri, C., & Mahfoudhe, K. ben. (2020). Deep Multi-Scale 3D Convolutional Neural Network (CNN) for MRI Gliomas Brain Tumor Classification. Journal of Digital Imaging, 33(4), 903–915. https://doi.org/10.1007/s10278-020-00347-9
Deepak, S., & Ameer, P. M. (2019). Brain tumor classification using deep CNN features via transfer learning. Computers in Biology and Medicine, 111. https://doi.org/10.1016/j.compbiomed.2019.103345
Varshney, K., & Mishra, K. (2022). An Analysis of Health Benefits of Carrot. International Journal of Innovative Research in Engineering & Management, 211–214. https://doi.org/10.55524/ijirem.2022.9.1.40
Puneet, Kumar, R., & Gupta, M. (2022). Optical coherence tomography image based eye disease detection using deep convolutional neural network. Health Information Science and Systems, 10(1). https://doi.org/10.1007/s13755-022-00182-y
Mahdi Abdulkareem, N., & Mohsin Abdulazeez, A. (2021). Machine Learning Classification Based on Radom Forest Algorithm: A Review. https://doi.org/10.5281/zenodo.4471118
Kim, M., Yun, J., Cho, Y., Shin, K., Jang, R., Bae, H. J., & Kim, N. (2019). Deep learning in medical imaging. In Neurospine (Vol. 16, Issue 4, pp. 657–668). Korean Spinal Neurosurgery Society. https://doi.org/10.14245/ns.1938396.198
Deepak, S., & Ameer, P. M. (2021). Automated Categorization of Brain Tumor from MRI Using CNN features and SVM. Journal of Ambient Intelligence and Humanized Computing, 12(8), 8357–8369. https://doi.org/10.1007/s12652-020-02568-w
Pisner, D. A., & Schnyer, D. M. (2019). Support vector machine. In Machine Learning: Methods and Applications to Brain Disorders (pp. 101–121). Elsevier. https://doi.org/10.1016/B978-0-12-815739-8.00006-7
Lu, W., Li, J., Wang, J., & Qin, L. (2021). A CNN-BiLSTM-AM method for stock price prediction. In Neural Computing and Applications (Vol. 33, Issue 10, pp. 4741–4753). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/s00521-020-05532-z
Kumar, R., Joshi, S., & Dwivedi, A. (2021). CNN-SSPSO: A Hybrid and Optimized CNN Approach for Peripheral Blood Cell Image Recognition and Classification. International Journal of Pattern Recognition and Artificial Intelligence, 35(5). https://doi.org/10.1142/S0218001421570044
Thaiyalnayaki, K. (2021). Classification of diabetes using deep learning and svm techniques. International Journal of Current Research and Review, 13(1), 146–149. https://doi.org/10.31782/IJCRR.2021.13127
DOI: https://doi.org/10.30998/semnasristek.v8i01.7129
Refbacks
- There are currently no refbacks.
Prosiding SEMNAS RISTEK indexed by: