Multi-Type Interdependent Feature Analysis Based on Hybrid Neural Networks for Computer-Aided Diagnosis of Epidermal Growth Factor Receptor Mutations

Multi-Type Interdependent Feature Analysis Based on Hybrid Neural Networks for Computer-Aided Diagnosis of Epidermal Growth Factor Receptor Mutations

The mutation status of the epidermal growth factor receptor (EGFR) is an important clinical reference indicator for lung cancer diagnosis and treatment. However, the extraction of effective discriminative features for non-invasive computer-aided EGFR mutation prediction still poses a big challenge....

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Bibliographic Details
Main Authors: Ruoxi Qin, Zhenzhen Wang, Kai Qiao, Jinjin Hai, Lingyun Jiang, Jian Chen, Xilong Pei, Dapeng Shi, Bin Yan
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Computer-aided diagnosis
epidermal growth factor receptor
mutation
long short-term memory
multi-type features
Online Access:https://ieeexplore.ieee.org/document/8979442/

Internet

https://ieeexplore.ieee.org/document/8979442/

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