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.
Breast cancer histopathological image classification using convolutional neural networks with small SE-ResNet module
