A Data Discovery on Prostat Cancer Through Meta Hybrid Learning Using Ensemble Deep Autoencoder

Purpose of Study is focus onthe early diagnosis of prostate cancer, which is a major concern for the health of people all over the world, requires the use of cutting-edge imaging techniques such as magnetic resonance imaging (MRI). The methodology and techniques used in this research presents a novel approach that makes use of ensemble deep autoencoders as a means of detecting prostate cancer in MRI scans. The purpose of the ensemble framework is to improve the robustness and accuracy of the detection process by capitalizing on the strengths of many autoencoder models. Major Findings of this research is the autoencoders have been optimized for the process of extracting complex characteristics from MRI scans. The approach known as ensemble learning takes all these distinct representations and combines them into a single diagnostic model that is both more comprehensive and reliable. In summary, Experiment results indicate that the proposed ensemble deep autoencoder system performs better than individual models, indicating its potential as a reliable instrument for the accurate early identification of prostate cancer in MRI scans.