Abstract

Advancements in machine learning have revolutionized tumor diagnosis, particularly through the analysis of glycoprotein microarray images. This study explores the application of machine learning algorithms for achieving highprecision, multipurpose tumor diagnosis. Glycoproteins, due to their varied expression patterns in different cancers, serve as crucial biomarkers. By leveraging machine learning techniques such as convolutional neural networks (CNNs) and support vector machines (SVMs), this research aims to enhance the accuracy and efficiency of tumor classification. The methodology involves preprocessing of glycoprotein microarray images to extract informative features, followed by training and validation of the models on comprehensive datasets. Evaluation metrics such as sensitivity, specificity, and area under the curve (AUC) are utilized to assess the performance of the models. Results indicate promising outcomes in terms of both diagnostic accuracy and computational efficiency, highlighting the potential of machine learning in transforming tumor diagnosis through glycoprotein microarray analysis. Future directions involve scaling the model to clinical settings and integrating real-time data for enhanced decision support in oncology practice.