N-Crop Based Image Division in Deep Learning with Medical Image

Ju Hyeon Lee; Dongho Lee; Yan Li; Byeong Seok Shin

doi:10.1007/978-981-32-9244-4_30

N-Crop Based Image Division in Deep Learning with Medical Image

Ju Hyeon Lee, Dongho Lee, Yan Li, Byeong Seok Shin

Inha University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

In this paper, we propose an image division technique that can solve the problem of resolution reduction due to model structure and the lack of data caused by the characteristic of medical images. To verify this technique, we compared the performance of traditional full image learning and divided image learning. As a result, it is confirmed that the image division technique can proceed X-ray image deep learning more stable and is effective in predicting tuberculosis detection with higher accuracy.

Original language	English
Title of host publication	Advanced Multimedia and Ubiquitous Engineering - MUE/FutureTech 2019
Editors	Laurence T. Yang, Fei Hao, Young-Sik Jeong, James J. Park
Publisher	Springer Verlag
Pages	213-218
Number of pages	6
ISBN (Print)	9789813292437
DOIs	https://doi.org/10.1007/978-981-32-9244-4_30
State	Published - 2020
Event	13th International Conference on Multimedia and Ubiquitous Engineering, MUE 2019 and 14th International Conference on Future Information Technology, Future Tech 2019 - Xian, China Duration: 24 Apr 2019 → 26 Apr 2019

Publication series

Name	Lecture Notes in Electrical Engineering
Volume	590
ISSN (Print)	1876-1100
ISSN (Electronic)	1876-1119

Conference

Conference	13th International Conference on Multimedia and Ubiquitous Engineering, MUE 2019 and 14th International Conference on Future Information Technology, Future Tech 2019
Country/Territory	China
City	Xian
Period	24/04/19 → 26/04/19

Bibliographical note

Publisher Copyright:
© 2020, Springer Nature Singapore Pte Ltd.

Keywords

CNN
Data augmentation
Deep learning
Medical image

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-981-32-9244-4_30

Cite this

Lee, J. H., Lee, D., Li, Y., & Shin, B. S. (2020). N-Crop Based Image Division in Deep Learning with Medical Image. In L. T. Yang, F. Hao, Y.-S. Jeong, & J. J. Park (Eds.), Advanced Multimedia and Ubiquitous Engineering - MUE/FutureTech 2019 (pp. 213-218). (Lecture Notes in Electrical Engineering; Vol. 590). Springer Verlag. https://doi.org/10.1007/978-981-32-9244-4_30

@inproceedings{2335b2dd6bc440318d7084bd59238b92,

title = "N-Crop Based Image Division in Deep Learning with Medical Image",

abstract = "In this paper, we propose an image division technique that can solve the problem of resolution reduction due to model structure and the lack of data caused by the characteristic of medical images. To verify this technique, we compared the performance of traditional full image learning and divided image learning. As a result, it is confirmed that the image division technique can proceed X-ray image deep learning more stable and is effective in predicting tuberculosis detection with higher accuracy.",

keywords = "CNN, Data augmentation, Deep learning, Medical image",

author = "Lee, {Ju Hyeon} and Dongho Lee and Yan Li and Shin, {Byeong Seok}",

note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Singapore Pte Ltd.; 13th International Conference on Multimedia and Ubiquitous Engineering, MUE 2019 and 14th International Conference on Future Information Technology, Future Tech 2019 ; Conference date: 24-04-2019 Through 26-04-2019",

year = "2020",

doi = "10.1007/978-981-32-9244-4_30",

language = "English",

isbn = "9789813292437",

series = "Lecture Notes in Electrical Engineering",

publisher = "Springer Verlag",

pages = "213--218",

editor = "Yang, {Laurence T.} and Fei Hao and Young-Sik Jeong and Park, {James J.}",

booktitle = "Advanced Multimedia and Ubiquitous Engineering - MUE/FutureTech 2019",

address = "Germany",

}

Lee, JH, Lee, D, Li, Y & Shin, BS 2020, N-Crop Based Image Division in Deep Learning with Medical Image. in LT Yang, F Hao, Y-S Jeong & JJ Park (eds), Advanced Multimedia and Ubiquitous Engineering - MUE/FutureTech 2019. Lecture Notes in Electrical Engineering, vol. 590, Springer Verlag, pp. 213-218, 13th International Conference on Multimedia and Ubiquitous Engineering, MUE 2019 and 14th International Conference on Future Information Technology, Future Tech 2019, Xian, China, 24/04/19. https://doi.org/10.1007/978-981-32-9244-4_30

N-Crop Based Image Division in Deep Learning with Medical Image. / Lee, Ju Hyeon; Lee, Dongho; Li, Yan et al.
Advanced Multimedia and Ubiquitous Engineering - MUE/FutureTech 2019. ed. / Laurence T. Yang; Fei Hao; Young-Sik Jeong; James J. Park. Springer Verlag, 2020. p. 213-218 (Lecture Notes in Electrical Engineering; Vol. 590).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - N-Crop Based Image Division in Deep Learning with Medical Image

AU - Lee, Ju Hyeon

AU - Lee, Dongho

AU - Li, Yan

AU - Shin, Byeong Seok

PY - 2020

Y1 - 2020

N2 - In this paper, we propose an image division technique that can solve the problem of resolution reduction due to model structure and the lack of data caused by the characteristic of medical images. To verify this technique, we compared the performance of traditional full image learning and divided image learning. As a result, it is confirmed that the image division technique can proceed X-ray image deep learning more stable and is effective in predicting tuberculosis detection with higher accuracy.

AB - In this paper, we propose an image division technique that can solve the problem of resolution reduction due to model structure and the lack of data caused by the characteristic of medical images. To verify this technique, we compared the performance of traditional full image learning and divided image learning. As a result, it is confirmed that the image division technique can proceed X-ray image deep learning more stable and is effective in predicting tuberculosis detection with higher accuracy.

KW - CNN

KW - Data augmentation

KW - Deep learning

KW - Medical image

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DO - 10.1007/978-981-32-9244-4_30

M3 - Conference contribution

AN - SCOPUS:85071870576

SN - 9789813292437

T3 - Lecture Notes in Electrical Engineering

SP - 213

EP - 218

BT - Advanced Multimedia and Ubiquitous Engineering - MUE/FutureTech 2019

A2 - Yang, Laurence T.

A2 - Hao, Fei

A2 - Jeong, Young-Sik

A2 - Park, James J.

PB - Springer Verlag

T2 - 13th International Conference on Multimedia and Ubiquitous Engineering, MUE 2019 and 14th International Conference on Future Information Technology, Future Tech 2019

Y2 - 24 April 2019 through 26 April 2019

ER -

N-Crop Based Image Division in Deep Learning with Medical Image

Abstract

Publication series

Conference

Bibliographical note

Keywords

UN SDGs

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