Multilayer grid XG Boost architecture based automatic osteosarcoma classification

Abstract

The application of machinelearning(ML) and deep learning (DL)methods might reduce the amount of time required by clinicians while simultaneously enhancing patient outcomes. The classification algorithm has to be provided with a huge amount of data in order to improve its accuracy. In this study, a combination of ML and DL is utilized to differentiate between images of normal and necrotic tissues utilizing a public database of osteosarcoma histological images. The data was initially preprocessed, and contour based threshold segmentation algorithms were performed. Next, the anomalous features are extracted using stochastic linear embedding-based feature extraction. Finally, stained photos are used to train the proposed multilayer grid XG Boost classifier, which improves output accuracy. The results of the experiments show that the suggested classifier is the most accurate one presently in use for categorizing illnesses. With H and E stained images, our improved model performed better at detecting osteosarcoma malignancy.