DETECTION AND CLASSIFICATION OF MITOTIC FIGURES IN GLIOMA USING ELECTROMAGNETIC SIGNALS.

Abstract

Gliomas are a type of brain tumor characterized by uncontrolled cell growth and division .Thedetection and classification of mitotic figures,which are indicative of cell proliferation,are crucial for diagnosing and treating glioma.Traditional methods of detecting mitotic figures rely on histopathological examination,which is time-consuming.labor-intensive,and prone to human error.To overcome these limitation, we propose the use of an electromagnetic sensor to detect and classify mitotic figures in glioma cell.Our system utilizes a novel approach that combines the sensitivity of electromagnetic sensor with advanced machine learning algorithms to accurately identify and classify mitotic figures.

The electromagnetic sensor used in our system is designed to detect the subtle changes in electromagnetic signals emitted by glioma cells as they undergo mitosis. The nsor is capable of detecting the unique electromagnetic signatures of mitotic figures, even in the presence of noise and artifacts.The detected signals are then processed using a machine learning algorithm that can classify the mitotic figures into different categories based on their morphology and characteristics.Our system has been trained on a dataset of glioma cell images and has achieved high accuracy in detecting and classifying mitotic figures.

Keywords