Publication

Abstract : Nuclei pathology is important for diagnosing various diseases and cancer. It is responsible for examining cellular structures found in tissue samples. Laboratories have the challenge of accurate nuclei classification in order to identify illnesses and support treatment. Here, we introduce a new strategy to identify four types of nuclei (,Other‘, ‘Inflammatory’, ‘Epithelial’ and ‘Spindle-Shaped’), using YOLOv9 deep learning. The present approach provides an overall accuracy of 98.9% in classifying nuclei, with other metrics also ranked strongly. A high rate of consistency is guaranteed by the use of this model, meaning that the associated expertise is capable of increasing the diagnosis aid in pathology. Brought by the increase in precision, diagnosis in all other fields is also able to be increased more quickly. The utilization of YOLOv9 opens up a better approach to faster pipelines of better quality for the diagnosis of patients, where reliability and speed are the key to help guide proper funding to lived improvement outcomes.