Osteosarcoma Pathological Classification using Statistical Features

Osteosarcoma is a type of bone cancer. Its diagnosis refers to anatomopathology by examining tissue samples under the microscope. The goal of this examination is to determine the morphology and behavior of cancer cells in order to determine the presence and extent of osteosarcoma in the patient. This paper focuses on the use of statistical features to classify osteosarcoma disease. The main idea is to use advanced statistical analysis techniques to provide more accurate and effective analysis.By incorporating statistical features extracted from histopathology images, the study aims to develop a robust classification model based on a gradient boosting algorithm that can help accurately detect and classify osteosarcoma cases. The ultimate goal is to help improve diagnostic methods for osteosarcoma, potentially enhancing treatment options and outcomes for patients. The algorithm was applied to histopathological images of osteosarcoma, which consisted of three categories: viable tumors, non-viable tumors, and non-tumor tumors. The model exhibited impressive performance metrics, with an accuracy of 94.54%, precision of 94.33%, specificity of 93.91%, and sensitivity of 94.61%.