Examining the Use of Generative Adversarial Network for Predicting Tumor Malignancy

Abstract

This study's paper examines using a generative opposed network as an excellent way to predict the malignancy of tumors in a clinically applicable manner. The examination outcomes imply that the DCGAN-based total version can make surprisingly dependable predictions of tumor malignancy compared to other machine-mastering strategies. Furthermore, the authors additionally propose that the DCGAN-based total version may be hired in scientific applications with promising effects.