A Review on Occluded Object Detection and Deep Learning-Based Approach in Medical Imaging-Related Research

Abstract

Medical image occlusions can arise due to several factors, including anatomical features, imaging modality, and acquisition settings. Object occlusion occurrence has a very significant effect on detection accuracy in general and in medical cases, these effects hinder proper diagnosis and treatment plans that may be fatal for the patients. Thus, precise occluded object detection is imperative. This paper aims to review the various state-of-the-art models and approaches that had been proposed for the occluded object detection. The coverage of this paper includes occluded object detection models in other applications and medical imaging, its proposed Deep Learning implementations, hybrid Deep Learning, and statistical analysis that were integrated into Deep Learning models. It is found that, in overall, more annotated medical image datasets are required to reduce overfitting occurrence, numerous Deep Learning models and its hybrid combination’s applications yet to be tested of its limitations, and the extent of statistical analysis integration on Deep Learning models.