Ocular Structures Segmentation from Multi-sequences MRI Using 3D Unet with Fully Connected CRFs

2018 ◽  
pp. 167-175
Author(s):  
Huu-Giao Nguyen ◽  
Alessia Pica ◽  
Philippe Maeder ◽  
Ann Schalenbourg ◽  
Marta Peroni ◽  
...  
Keyword(s):  
Fully Connected

KLASIFIKASI CITRA GENUS PANTHERA MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK (CNN)

2019 ◽  
Vol 24 (3) ◽  
pp. 220-228
Author(s):  
Gusti Alfahmi Anwar ◽  
Desti Riminarsih
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Input Layer ◽  
Fully Connected

Panthera merupakan genus dari keluarga kucing yang memiliki empat spesies popular yaitu, harimau, jaguar, macan tutul, singa. Singa memiliki warna keemasan dan tidak memilki motif, harimau memiliki motif loreng dengan garis-garis panjang, jaguar memiliki tubuh yang lebih besar dari pada macan tutul serta memiliki motif tutul yang lebih lebar, sedangkan macan tutul memiliki tubuh yang sedikit lebih ramping dari pada jaguar dan memiliki tutul yang tidak terlalu lebar. Pada penelitian ini dilakukan klasifikasi genus panther yaitu harimau, jaguar, macan tutul, dan singa menggunakan metode Convolutional Neural Network. Model Convolutional Neural Network yang digunakan memiliki 1 input layer, 5 convolution layer, dan 2 fully connected layer. Dataset yang digunakan berupa citra harimau, jaguar, macan tutul, dan singa. Data training terdiri dari 3840 citra, data validasi sebanyak 960 citra, dan data testing sebanyak 800 citra. Hasil akurasi dari pelatihan model untuk training yaitu 92,31% dan validasi yaitu 81,88%, pengujian model menggunakan dataset testing mendapatan hasil 68%. Hasil akurasi prediksi didapatkan dari nilai F1-Score pada pengujian didapatkan sebesar 78% untuk harimau, 70% untuk jaguar, 37% untuk macan tutul, 74% untuk singa. Macan tutul mendapatkan akurasi terendah dibandingkan 3 hewan lainnya tetapi lebih baik dibandingkan hasil penelitian sebelumnya.

scholarly journals OPTWEB: A Lightweight Fully Connected Inter-FPGA Network for Efficient Collectives

2021 ◽  
pp. 1-1
Author(s):  
Kenji Mizutani ◽  
Hiroshi Yamaguchi ◽  
Yutaka Urino ◽  
Michihiro Koibuchi
Keyword(s):  
Fully Connected

scholarly journals Radar Emitter Identification Based on Fully Connected Spiking Neural Network

2021 ◽  
Vol 1914 (1) ◽  
pp. 012036
Author(s):  
LI Wei ◽  
Zhu Wei-gang ◽  
Pang Hong-feng ◽  
Zhao Hong-yu
Keyword(s):  
Neural Network ◽  
Spiking Neural Network ◽  
Fully Connected

scholarly journals Designing a Geo-Strategic Railway Freight Network in Brazil Using GIS

2020 ◽  
Vol 13 (1) ◽  
pp. 85
Author(s):  
Cassiano A. Isler ◽  
Yesid Asaff ◽  
Marin Marinov
Keyword(s):  
Transport Networks ◽  
Railway Network ◽  
The Sustainable Development ◽  
Railway Freight ◽  
Minimum Number ◽  
Technical Specifications ◽  
Future Demands ◽  
Terminal Operations ◽  
Fully Connected ◽  
South Brazil

The sustainable development of geo-strategic transport networks plays a key role to meet the current expansion of the demand for commerce and economic growth. In this paper, a new geo-strategic railway network for freight services is designed with the purpose of meeting the needs of current and future demands for freight transport in the state of Santa Catarina, South Brazil. The freight flows of bulk cargo, containers, and refrigerated and liquid cargo observed in 2005 and 2015 and expected for 2023 have been analyzed and assigned to a fully connected railway network. The number of trains to meet all the demands has been identified. The links that would have a minimum number of daily trains running on them have also been identified and analyzed. New assignments are proposed and visualized using GIS. Next, location and technical specifications of specialized intermodal terminals focused on the customers’ and operators’ needs are discussed. The study shows that technological specifications for terminal operations play an important role when dealing with multiple freight types and contribute to better use of the existing infrastructure.

scholarly journals Freely scalable and reconfigurable optical hardware for deep learning

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Liane Bernstein ◽  
Alexander Sludds ◽  
Ryan Hamerly ◽  
Vivienne Sze ◽  
Joel Emer ◽  
...  
Keyword(s):  
Neural Network ◽  
Energy Consumption ◽  
Data Transfer ◽  
Power Delivery ◽  
Optical Data ◽  
Optical Multicast ◽  
Optical Neural Network ◽  
Fully Connected ◽  
Management Power

AbstractAs deep neural network (DNN) models grow ever-larger, they can achieve higher accuracy and solve more complex problems. This trend has been enabled by an increase in available compute power; however, efforts to continue to scale electronic processors are impeded by the costs of communication, thermal management, power delivery and clocking. To improve scalability, we propose a digital optical neural network (DONN) with intralayer optical interconnects and reconfigurable input values. The path-length-independence of optical energy consumption enables information locality between a transmitter and a large number of arbitrarily arranged receivers, which allows greater flexibility in architecture design to circumvent scaling limitations. In a proof-of-concept experiment, we demonstrate optical multicast in the classification of 500 MNIST images with a 3-layer, fully-connected network. We also analyze the energy consumption of the DONN and find that digital optical data transfer is beneficial over electronics when the spacing of computational units is on the order of $$>10\,\upmu $$ > 10 μ m.

Explainable Fully Connected Visual Words for the Classification of Skin Cancer Confocal Images

2020 ◽  
Author(s):  
Athanasios Kallipolitis ◽  
Alexandros Stratigos ◽  
Alexios Zarras ◽  
Ilias Maglogiannis
Keyword(s):  
Skin Cancer ◽  
Visual Words ◽  
Confocal Images ◽  
Fully Connected

scholarly journals Automatic Segmentation of Choroid Layer Using Deep Learning on Spectral Domain Optical Coherence Tomography

2021 ◽  
Vol 11 (12) ◽  
pp. 5488
Author(s):  
Wei Ping Hsia ◽  
Siu Lun Tse ◽  
Chia Jen Chang ◽  
Yu Len Huang
Keyword(s):  
Optical Coherence Tomography ◽  
Deep Learning ◽  
Choroidal Thickness ◽  
Automatic Segmentation ◽  
Average Error ◽  
Good Prediction ◽  
Optical Coherence ◽  
Learning Method ◽  
Subfoveal Choroidal Thickness ◽  
Fully Connected

The purpose of this article is to evaluate the accuracy of the optical coherence tomography (OCT) measurement of choroidal thickness in healthy eyes using a deep-learning method with the Mask R-CNN model. Thirty EDI-OCT of thirty patients were enrolled. A mask region-based convolutional neural network (Mask R-CNN) model composed of deep residual network (ResNet) and feature pyramid networks (FPNs) with standard convolution and fully connected heads for mask and box prediction, respectively, was used to automatically depict the choroid layer. The average choroidal thickness and subfoveal choroidal thickness were measured. The results of this study showed that ResNet 50 layers deep (R50) model and ResNet 101 layers deep (R101). R101 U R50 (OR model) demonstrated the best accuracy with an average error of 4.85 pixels and 4.86 pixels, respectively. The R101 ∩ R50 (AND model) took the least time with an average execution time of 4.6 s. Mask-RCNN models showed a good prediction rate of choroidal layer with accuracy rates of 90% and 89.9% for average choroidal thickness and average subfoveal choroidal thickness, respectively. In conclusion, the deep-learning method using the Mask-RCNN model provides a faster and accurate measurement of choroidal thickness. Comparing with manual delineation, it provides better effectiveness, which is feasible for clinical application and larger scale of research on choroid.

Sign in / Sign up

Export Citation Format

Share Document