Open AccessConvolutional neural networks for computer-aided detection or diagnosis in medical image analysis: An overview
Author(s) -
Jun Gao,
Qian Jiang,
Bo Zhou,
Daozheng Chen
Publication year - 2019
Publication title -
mathematical biosciences and engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.451
H-Index - 45
eISSN - 1551-0018
pISSN - 1547-1063
DOI - 10.3934/mbe.2019326
Subject(s) - convolutional neural network , computer science , cad , computer aided diagnosis , artificial intelligence , deep learning , medical imaging , focus (optics) , machine learning , process (computing) , image processing , pattern recognition (psychology) , image (mathematics) , engineering drawing , physics , optics , engineering , operating system
Computer-aided detection or diagnosis (CAD) has been a promising area of research over the last two decades. Medical image analysis aims to provide a more efficient diagnostic and treatment process for the radiologists and clinicians. However, with the development of science and technology, data interpretation manually in the conventional CAD systems has gradually become a challenging task. Deep learning methods, especially convolutional neural networks (CNNs), are successfully used as tools to solve this problem. This includes applications such as breast cancer diagnosis, lung nodule detection and prostate cancer localization. In this overview, the current state-of-the-art medical image analysis techniques in CAD research are presented, which focus on the convolutional neural network (CNN) based methods. The commonly used medical image databases in literature are also listed. It is anticipated that this paper can provide researchers in radiomics, precision medicine, and imaging grouping with a systematic picture of the CNN-based methods used in CAD research.