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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.