A rotation and translation invariant method for 3D organ image classification using deep convolutional neural networks.

ABSTRACT: Three-dimensional (3D) medical image classification is useful in applications such as disease diagnosis and content-based medical image retrieval. It is a challenging task due to several reasons. First, image intensity values are vastly different depending on the image modality. Second, intensity values within the same image modality may vary depending on the imaging machine and artifacts may also be introduced in the imaging process. Third, processing 3D data requires high computational power. In recent years, significant research has been conducted in the field of 3D medical image classification. However, most of these make assumptions about patient orientation and imaging direction to simplify the problem and/or work with the full 3D images. As such, they perform poorly when these assum

SUBMITTER: Islam KT 

PROVIDER: S-EPMC7924426 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature