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

[41] Detection algorithm of pulmonary nodules based on deep learning [J].

Zhang, Weiguo ;

Cui, Linfang .

2021 2ND INTERNATIONAL CONFERENCE ON BIG DATA & ARTIFICIAL INTELLIGENCE & SOFTWARE ENGINEERING (ICBASE 2021), 2021, :185-188

[42] Lung Nodule Classification on Computed Tomography Images Using Deep Learning [J].

Amrita Naik ;

Damodar Reddy Edla .

Wireless Personal Communications, 2021, 116 :655-690

[43] A deep learning approach for nucleus segmentation and tumor classification from lung histopathological images [J].

Jaisakthi, S. M. ;

Desingu, Karthik ;

Mirunalini, P. ;

Pavya, S. ;

Priyadharshini, N. .

NETWORK MODELING AND ANALYSIS IN HEALTH INFORMATICS AND BIOINFORMATICS, 2023, 12 (01)

[44] Performance of Deep-Learning Solutions on Lung Nodule Malignancy Classification: A Systematic Review [J].

Liang, Hailun ;

Hu, Meili ;

Ma, Yuxin ;

Yang, Lei ;

Chen, Jie ;

Lou, Liwei ;

Chen, Chen ;

Xiao, Yuan .

LIFE-BASEL, 2023, 13 (09)

[45] Mammogram classification using Deep learning features [J].

Gardezi, Syed Jamal Safdar ;

Awais, Muhammad ;

Faye, Ibrahima ;

Meriaudeau, Fabrice .

2017 IEEE INTERNATIONAL CONFERENCE ON SIGNAL AND IMAGE PROCESSING APPLICATIONS (ICSIPA), 2017, :485-488

[46] Distracted driver classification using deep learning [J].

Alotaibi, Munif ;

Alotaibi, Bandar .

SIGNAL IMAGE AND VIDEO PROCESSING, 2020, 14 (03) :617-624

[47] Pneumonia classification using quaternion deep learning [J].

Singh, Sukhendra ;

Tripathi, B. K. .

MULTIMEDIA TOOLS AND APPLICATIONS, 2022, 81 (02) :1743-1764

[48] Distracted driver classification using deep learning [J].

Munif Alotaibi ;

Bandar Alotaibi .

Signal, Image and Video Processing, 2020, 14 :617-624

[49] Photo quality classification using deep learning [J].

Golchubian, Arash ;

Marques, Oge ;

Nojoumian, Mehrdad .

MULTIMEDIA TOOLS AND APPLICATIONS, 2021, 80 (14) :22193-22208

[50] Photo quality classification using deep learning [J].

Arash Golchubian ;

Oge Marques ;

Mehrdad Nojoumian .

Multimedia Tools and Applications, 2021, 80 :22193-22208

← 1 2 3 4 5 →