We developed a Particle Swarm Optimization (PSO) inspired ontology to resolve the object classification problem, using cervical cancer cell images as a case study. Implemented in Protégé 5.5, the ontology was integrated with two datasets for cervical cancer images namely Herlev and SipakMed datasets to classify images into three categories: abnormal, normal, and benign. Python scripts were employed to extract key features from the two datasets and upload them into the ontology. Trained on SipakMed and tested on Herlev the model achieved a classification accuracy of 87.11%, with a precision of 96% and a recall of 96% for normal cells, and a precision of 95% and recall of 91% for abnormal cells. However, classification of benign cells still showed lower precision even when tested on each dataset independently or combined this could also be due to overlapping features with other classes and limited benign cell images. This study highlights the potential of PSO-inspired ontologies for effective object classification.

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