Convolutional neural networks for computer-aided detection or diagnosis in medical image analysis: An overview

Jun Gao; Qian Jiang; Bo Zhou; Daozheng Chen; Jun Gao; Qian Jiang; Bo Zhou; Daozheng Chen

doi:10.3934/mbe.2019326

Mathematical Biosciences and Engineering

2019, Volume 16, Issue 6: 6536-6561. doi: 10.3934/mbe.2019326

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Review Special Issues

Convolutional neural networks for computer-aided detection or diagnosis in medical image analysis: An overview

1.
College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China.
2.
Shanghai University of Medicine & Health Science, Shanghai 201308, China

Received: 03 March 2019 Accepted: 11 June 2019 Published: 15 July 2019

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.
- computer-aided detection,
- computer-aided diagnosis,
- convolutional neural networks,
- deep learning,
- medical image analysis
Citation: Jun Gao, Qian Jiang, Bo Zhou, Daozheng Chen. Convolutional neural networks for computer-aided detection or diagnosis in medical image analysis: An overview[J]. Mathematical Biosciences and Engineering, 2019, 16(6): 6536-6561. doi: 10.3934/mbe.2019326

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Abstract

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.

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