New Deep Learning Models for Medical Imaging: Deep Belief Network, GAN, Autoencoder

Abstract

Deep learning (DL) is being utilized in different clinical imaging process and has accomplished promising results. Nonetheless, incorporating DL in clinical imaging also presents significant challenges. This research study presents the characteristics of clinical imaging that features both clinical requirements and specialized challenges and depicts how the patterns in DL are resolving these issues. This study presents a few contextual investigations that are commonly found, including advanced pathology and respiratory, chest, brain, and ophthalmology imaging. Rather than presenting a thorough writing review, this study highlights some notable exploration aspects associated with the contextual analysis. This study is concluded with a discussion and stating some possible future research directions.

Ähnliche Arbeiten