Graph Neural Networks for the Cross-Domain Histopathological Image Classification

2021 ◽  
Author(s):  
Chang Cai ◽  
Dou Xu ◽  
Chaowei Fang ◽  
Meng Yang ◽  
Zhongyu Li
Keyword(s):  
Neural Networks ◽  
Image Classification ◽  
Cross Domain ◽  
Histopathological Image ◽  
The Cross ◽  
Histopathological Image Classification ◽  
Graph Neural Networks

Breast Cancer Histopathological Image Classification Using Convolutional Neural Networks

2021 ◽  
pp. 183-195
Author(s):  
Ankita Adhikari ◽  
Ashesh Roy Choudhuri ◽  
Debanjana Ghosh ◽  
Neela Chattopadhyay ◽  
Rupak Chakraborty
Keyword(s):  
Breast Cancer ◽  
Neural Networks ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Histopathological Image ◽  
Histopathological Image Classification

Feature Selection for Histopathological Image Classification using levy Flight Salp Swarm Optimizer

2019 ◽  
Vol 12 (4) ◽  
pp. 329-337 ◽  
Author(s):  
Venubabu Rachapudi ◽  
Golagani Lavanya Devi
Keyword(s):  
Feature Selection ◽  
Image Classification ◽  
Feature Selection Method ◽  
Selection Method ◽  
Lévy Flight ◽  
Levy Flight ◽  
Local Optima ◽  
Histopathological Image ◽  
Surf Features ◽  
Histopathological Image Classification

Background: An efficient feature selection method for Histopathological image classification plays an important role to eliminate irrelevant and redundant features. Therefore, this paper proposes a new levy flight salp swarm optimizer based feature selection method. Methods: The proposed levy flight salp swarm optimizer based feature selection method uses the levy flight steps for each follower salp to deviate them from local optima. The best solution returns the relevant and non-redundant features, which are fed to different classifiers for efficient and robust image classification. Results: The efficiency of the proposed levy flight salp swarm optimizer has been verified on 20 benchmark functions. The anticipated scheme beats the other considered meta-heuristic approaches. Furthermore, the anticipated feature selection method has shown better reduction in SURF features than other considered methods and performed well for histopathological image classification. Conclusion: This paper proposes an efficient levy flight salp Swarm Optimizer by modifying the step size of follower salp. The proposed modification reduces the chances of sticking into local optima. Furthermore, levy flight salp Swarm Optimizer has been utilized in the selection of optimum features from SURF features for the histopathological image classification. The simulation results validate that proposed method provides optimal values and high classification performance in comparison to other methods.

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