Hybrid DSSCS and convolutional neural network for peripheral blood cell recognition system

In this study, an efficient a deep learning architecture‐based peripheral blood cell image recognition and classification is proposed using hybrid disruption‐based salp‐swarm and cat swarm (DSSCS)‐based optimized convolutional neural networks (DSSCSCNNs) method. The DSSCSCNN method is employed to overcome the hyperparameter problem in CNN and it also helps this model to work on small peripheral blood cell data sets. In the DSSCSCNN method, the authors develop a binary coding technique that converts parameter tuning problems into an optimization problem. The original salp swarm algorithm is enhanced using a disruptive operator and salp swarm optimization algorithm to form the novel DSSCS algorithm which increases the diversity of the search space by providing higher classification accuracy. In this study, the CNNs use Vgg‐16 architecture is used for training purposes. The global classification accuracy obtained when trained with the Vgg‐16 model is 97%. This method establishes a fine‐tuning process to develop a classifier trained using 15,976 images acquired from clinical practice. The proposed model gives improved performance in terms of accuracy, specificity, and sensitivity. In the WBC determination, the proposed approach has shown 100% achievement. It also provides the best overall classification accuracy of 99%.