Detection and Diagnosis of Pancreatic Tumor Using Convolutional Neural Network and Hybrid Particle Swarm Optimization Algorithm (CNN- HPSO) For Image Classification

Main Article Content

S. Arulmozhi, Dr. R. Shankar, Dr. S. Duraisamy

Abstract

Deep Neural Networks (DNNs) offer better performance in contrast to conventional Machine Learning (ML) techniquesin dealing with real-world applications.However, operational DNNs are based on the knowledge gained. More amounts of time and computational resources are consumed in this technique. In this paper, a method based on Particle Swarm Optimization (PSO) is propounded. This method is proficient in offering speedy convergence when compared to deep Convolutional Neural Networks (CNNs) models for classifying images. A unique encoding strategy along with a velocity operator is developed by incorporating the concepts of PSO with CNN. Experimental results have proved that PSO-CNN outperforms other existing methodologiesbased on Accuracy, Precision, Recall and F-measure.

Article Details

How to Cite
S. Arulmozhi, Dr. R. Shankar, Dr. S. Duraisamy. (2021). Detection and Diagnosis of Pancreatic Tumor Using Convolutional Neural Network and Hybrid Particle Swarm Optimization Algorithm (CNN- HPSO) For Image Classification. Annals of the Romanian Society for Cell Biology, 1403–1419. Retrieved from https://annalsofrscb.ro/index.php/journal/article/view/4582
Section
Articles