Classification of lung nodules using deep learning

被引：0

作者：

Kwajiri T. ^{[1
]}

Tezuka T. ^{[2
]}

机构：

[1] Department of Knowledge and Library Information Science, Faculty of Information, University of Tsukuba, Tsukuba

[2] Faculty of Library, Information, and Media Science, University of Tsukuba, Tsukuba

来源：

Transactions of Japanese Society for Medical and Biological Engineering | 2017年 / 55卷 / Proc期

关键词：

Cancer; Convolutional neural network; Deep learning; Deep neural network; Lung nodules; Residual network;

D O I：

10.11239/jsmbe.55Annual.516

中图分类号：

学科分类号：

摘要：

Deep learning methods such as the Convolutional Neural Network and the Residual Network were applied to CT scan images in order to classify whether lung nodules become cancerous or not. Especially, the effect of changing the number of layers in the Residual Network was. Experiment were carried out using several models having these two network architectures and consisting of different numbers of layers and parameters. © 2017, Japan Soc. of Med. Electronics and Biol. Engineering. All rights reserved.

引用

页码：516 / 517

页数：1

共 50 条

[21] A Novel Deep-Learning-Based CADx Architecture for Classification of Thyroid Nodules Using Ultrasound Images
Goreke, Volkan
INTERDISCIPLINARY SCIENCES-COMPUTATIONAL LIFE SCIENCES, 2023, 15 (03) : 360 - 373
[22] A Hybrid deep learning model for effective segmentation and classification of lung nodules from CT images
Murugesan, Malathi
Kaliannan, Kalaiselvi
Balraj, Shankarlal
Singaram, Kokila
Kaliannan, Thenmalar
Albert, Johny Renoald
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, 2022, 42 (03) : 2667 - 2679
[23] Deep Transfer Learning Strategy for Invasive Lung Adenocarcinoma Classification Appearing as Ground Glass Nodules
Ma, Chenchen
Yue, Shihong
Li, Qi
2021 IEEE INTERNATIONAL INSTRUMENTATION AND MEASUREMENT TECHNOLOGY CONFERENCE (I2MTC 2021), 2021,
[24] Detection of Lung Nodules in Micro-CT Imaging Using Deep Learning
Holbrook, Matthew D.
Clark, Darin P.
Patel, Rutulkumar
Qi, Yi
Bassil, Alex M.
Mowery, Yvonne M.
Badea, Cristian T.
TOMOGRAPHY, 2021, 7 (03) : 358 - 372
[25] Detection of Lung Nodules from Temporal Subtraction Image Using Deep Learning
Tamai, Kohei
Miyake, Noriaki
Lu, Humin
Kim, Hyoungseop
Murakami, Seiichi
Aoki, Takatoshi
Kido, Shoji
2019 19TH INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION AND SYSTEMS (ICCAS 2019), 2019, : 1033 - 1036
[26] Classification of Lung and Colon Cancer Using Deep Learning Method
Provath, Md Al-Mamun
Deb, Kaushik
Jo, Kang-Hyun
FRONTIERS OF COMPUTER VISION, IW-FCV 2023, 2023, 1857 : 56 - 70
[27] Detection and classification of lung nodules in chest X-ray images using deep convolutional neural networks
Mendoza, Julio
Pedrini, Helio
COMPUTATIONAL INTELLIGENCE, 2020, 36 (02) : 370 - 401
[28] Impact of localized fine tuning in the performance of segmentation and classification of lung nodules from computed tomography scans using deep learning
Cai, Jingwei
Guo, Lin
Zhu, Litong
Xia, Li
Qian, Lingjun
Lure, Yuan-Ming Fleming
Yin, Xiaoping
FRONTIERS IN ONCOLOGY, 2023, 13
[29] Automated Lung Nodule Detection and Classification Using Deep Learning Combined with Multiple Strategies
Nasrullah, Nasrullah
Sang, Jun
Alam, Mohammad S.
Mateen, Muhammad
Cai, Bin
Hu, Haibo
SENSORS, 2019, 19 (17)
[30] Weakly-supervised learning for lung carcinoma classification using deep learning
Kanavati, Fahdi
Toyokawa, Gouji
Momosaki, Seiya
Rambeau, Michael
Kozuma, Yuka
Shoji, Fumihiro
Yamazaki, Koji
Takeo, Sadanori
Iizuka, Osamu
Tsuneki, Masayuki
SCIENTIFIC REPORTS, 2020, 10 (01)

← 1 2 3 4 5 →