DenseNet-CNN Architectural Model for Detection of Abnormality in Acute Pulmonary Edema
(1) Universitas Kristen Krida Wacana
(*) Corresponding Author
DOI: https://doi.org/10.23917/khif.v7i2.13455
Abstract
Keywords
Full Text:
PDFReferences
S. H. Rampengan, “EDEMA PARU KARDIOGENIK AKUT,” J. BIOMEDIK, 2014, doi: 10.35790/jbm.6.3.2014.6320.
H. Nendrastuti and M. Soetomo, “Edema Paru Akut Kardiogenik Dan Non Kardiogenik,” Maj. Kedokt. Respirasi, 2010.
M. Irawaty, “Penatalaksanaan Edema Paru pada Kasus VSD dan Sepsis VAP Treatment of Lung Oedema in VSD and VAP Sepsis,” Anest. Crit. Care, 2010.
A. Jatu and Lusiana, “Peranan Epitel Alveoli pada Edema Paru Non-kardiogenik,” Ckd, 2015.
C. Hayat and B. Abian, “The modeling of artificial neural network of early diagnosis for malnutrition with backpropagation method,” 2018, doi: 10.1109/IAC.2018.8780505.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” 2016, doi: 10.1109/CVPR.2016.90.
A. K. Jaiswal, P. Tiwari, S. Kumar, D. Gupta, A. Khanna, and J. J. P. C. Rodrigues, “Identifying pneumonia in chest X-rays: A deep learning approach,” Meas. J. Int. Meas. Confed., 2019, doi: 10.1016/j.measurement.2019.05.076.
G. Huang, S. Liu, L. Van Der Maaten, and K. Q. Weinberger, “CondenseNet: An Efficient DenseNet Using Learned Group Convolutions,” 2018, doi: 10.1109/CVPR.2018.00291.
G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, “Densely connected convolutional networks,” 2017, doi: 10.1109/CVPR.2017.243.
T. NURHIKMAT, “IMPLEMENTASI DEEP LEARNING UNTUK IMAGE CLASSIFICATION MENGGUNAKAN ALGORITMA CONVOLUTIONAL NEURAL NETWORK (CNN) PADA CITRA WAYANG GOLEK,” UNIVERSITAS ISLAM INDONESIA. 2018.
S. Albawi, T. A. Mohammed, and S. Al-Zawi, “Understanding of a convolutional neural network,” 2018, doi: 10.1109/ICEngTechnol.2017.8308186.
X. Wang, Y. Peng, L. Lu, Z. Lu, M. Bagheri, and R.
M. Summers, “ChestX-ray8: Hospital-scale chest X-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases,” 2017, doi: 10.1109/CVPR.2017.369.
E. T. Nader, “Chest X-ray interpretation,” in Perioperative Assessment of the Maxillofacial Surgery Patient: Problem-based Patient Management, 2018.
G. Huang, S. Liu, L. Van Der Maaten, and K. Q. Weinberger, “CondenseNet: An efficient densenet using learned group convolutions,” arXiv. 2017.
S. Ramiz and M. Rajpurkar, “Pulmonary Embolism in Children,” Pediatric Clinics of North America. 2018, doi: 10.1016/j.pcl.2018.02.002.
Y. Bengio, “Learning deep architectures for AI,” Found. Trends Mach. Learn., 2009, doi: 10.1561/2200000006.
P. Rajpurkar et al., “CheXNet: Radiologist-level pneumonia detection on chest X-rays with deep learning,” arXiv. 2017.
N. Kalchbrenner, E. Grefenstette, and P. Blunsom, “A convolutional neural network for modelling sentences,” 2014, doi: 10.3115/v1/p14-1062.
Y. Zhang, J. Gao, and H. Zhou, “Breeds Classification with Deep Convolutional Neural Network,” 2020, doi: 10.1145/3383972.3383975.
C. Neural and N. Accelerator, “ISAAC : A Convolutional Neural Network Accelerator with I n- S itu A nalog A rithmetic in C rossbars,” Iscas, 2016.
R. M. PRASMATIO, B. Rahmat, and I. Yuniar, “Deteksi Dan Pengenalan Ikan Menggunakan Algoritma Convolutional Neural Network,” J. Inform. dan Sist. Inf., 2020.
Article Metrics
Abstract view(s): 396 time(s)PDF: 313 time(s)
Refbacks
- There are currently no refbacks.