Convolutional Neural Networks in Medical Imaging: A Review

被引：1

作者：

Lin, Anjie ^{[1
]}

Su, Bianping ^{[1
]}

Ning, Yihe ^{[2
]}

Zhang, Longqing ^{[1
]}

He, Yantao ^{[1
]}

机构：

[1] Guangdong Univ Sci & Technol, 99 Xihu Rd, Dongguan 523083, Peoples R China

[2] Xijing Hosp, 127 West Rd, Xian 710032, Peoples R China

来源：

ADVANCES IN SWARM INTELLIGENCE, PT II, ICSI 2024 | 2024年 / 14789卷

关键词：

Convolutional Neural Networks; Medical Imaging; Deep Learning; FUNCTIONAL ARCHITECTURE; RECEPTIVE-FIELDS; NEOCOGNITRON;

D O I：

10.1007/978-981-97-7184-4_35

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This review article traces the trajectory of Convolutional Neural Networks, providing an in-depth analysis of their foundational components: convolutional, pooling, and fully connected layers, and their integral function in feature extraction and image classification. It offers a comprehensive survey of landmark architectures such as LeNet, AlexNet, GoogLeNet, VGGNet, ResNet, ShuffleNet, and EfficientNet, examining their variants and their significant contributions to medical image processing. The review corroborates the transformative impact of CNNs in pivotal medical applications, including cancer detection, Alzheimer's diagnosis, and brain tumor identification. Additionally, It also examines the current challenges and issues, and outlines potential future directions for the field's evolution.

引用

页码：419 / 430

页数：12

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