TPCNN: Two-path convolutional neural network for tumor and liver segmentation in CT images using a novel encoding approach-Reference-Cited by-同舟云学术

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TPCNN: Two-path convolutional neural network for tumor and liver segmentation in CT images using a novel encoding approach

Published:2021-11 Issue: Volume:183 Page:115406
ISSN:0957-4174
Container-title:Expert Systems with Applications
language:en
Short-container-title:Expert Systems with Applications

Author:

Aghamohammadi Amirhossein,Ranjbarzadeh Ramin,Naiemi Fatemeh,Mogharrebi Marzieh,Dorosti Shadi,Bendechache Malika

Funder

Science Foundation Ireland

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Science Applications,General Engineering

Reference87 articles.

1. Affonso, C., Rossi, A.L.D., Vieira, F.H.A. & de Carvalho, A.C.P. de L.F. 2017. Deep learning for biological image classification. Expert Systems with Applications 85: 114–122.

2. Deep Belief Network Modeling for Automatic Liver Segmentation;Ahmad;IEEE Access,2019

3. de Assis Neto, S.R., Santos, G.L., da Silva Rocha, E., Bendechache, M., Rosati, P., Lynn, T. & Takako Endo, P. 2020. Detecting Human Activities Based on a Multimodal Sensor Data Set Using a Bidirectional Long Short-Term Memory Model: A Case Study. Studies in Systems, Decision and Control, hlm. 31–51. Springer.

4. Semi-automated segmentation of single and multiple tumors in Liver CT Images Using Entropy-Based Fuzzy Region Growing;Baâzaoui;IRBM,2017

5. Bae, Y.-K., Lee, J.-W. & Hong, S. 2020. Effects of image distortion and Hounsfield unit variations on radiation treatment plans: An extended field-of-view reconstruction in a large bore CT scanner.

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