Learning deep neural networks' architectures using differential evolution. Case study: Medical imaging processing.

ABSTRACT: The COVID-19 pandemic has changed the way we practice medicine. Cancer patient and obstetric care landscapes have been distorted. Delaying cancer diagnosis or maternal-fetal monitoring increased the number of preventable deaths or pregnancy complications. One solution is using Artificial Intelligence to help the medical personnel establish the diagnosis in a faster and more accurate manner. Deep learning is the state-of-the-art solution for image classification. Researchers manually design the structure of fix deep learning neural networks structures and afterwards verify their performance. The goal of this paper is to propose a potential method for learning deep network architectures automatically. As the number of networks architectures increases exponentially with the number of convolut

SUBMITTER: Belciug S 

PROVIDER: S-EPMC9112664 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature