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CNN based Lung Indexing Method for DICOM CT Image

Authors:

Chulwoong Choi,

Sungwoong Yeom,

Hieyong Jeong,

Kyungbaek KimAuthors Info & Claims

SMA 2020: The 9th International Conference on Smart Media and Applications

Pages 281 - 284

https://doi.org/10.1145/3426020.3426092

Published: 04 November 2021 Publication History

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Abstract

PET-CT medical images are widely used for the purpose of early diagnosis of cancer. PET-CT Imaging is a method of examing cancer by photographing the entire body and a larget set of DICOM images are generated for one patient such as 300 images. Since each major doctors focus on different area among this large set of images, automatic indexing method for the focused area is required. In this paper, we propose a CNN-based Lung Indexing Method that can index the image where the lung starts and the image where the lung ends among the whole body DICOM images. The indexed result and the actual answer were compared by using Intersection over Union (IoU). It is confirmed that the accuracy becomes highest at 70% when the start and end index numbers were extracted by using the proposed method with the median value option.

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Zhou FHu SWan XLu ZWu J(2023)Diplin: A Disease Risk Prediction Model Based on EfficientNetV2 and Transfer Learning Applied to Nursing HomesElectronics10.3390/electronics1212258112:12(2581)Online publication date: 7-Jun-2023
https://doi.org/10.3390/electronics12122581
Kolekar SChen HKim K(2023)Design of Precision Medicine Web-service Platform Towards Health Care Digital Twin2023 Fourteenth International Conference on Ubiquitous and Future Networks (ICUFN)10.1109/ICUFN57995.2023.10199942(843-848)Online publication date: 4-Jul-2023
https://doi.org/10.1109/ICUFN57995.2023.10199942

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CNN based Lung Indexing Method for DICOM CT Image

Index terms have been assigned to the content through auto-classification.

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SMA 2020: The 9th International Conference on Smart Media and Applications

September 2020

491 pages

ISBN:9781450389259

DOI:10.1145/3426020

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Published: 04 November 2021

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National Research Foundation of Korea

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SMA 2020

SMA 2020: The 9th International Conference on Smart Media and Applications

September 17 - 19, 2020

Jeju, Republic of Korea

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Zhou FHu SWan XLu ZWu J(2023)Diplin: A Disease Risk Prediction Model Based on EfficientNetV2 and Transfer Learning Applied to Nursing HomesElectronics10.3390/electronics1212258112:12(2581)Online publication date: 7-Jun-2023
https://doi.org/10.3390/electronics12122581
Kolekar SChen HKim K(2023)Design of Precision Medicine Web-service Platform Towards Health Care Digital Twin2023 Fourteenth International Conference on Ubiquitous and Future Networks (ICUFN)10.1109/ICUFN57995.2023.10199942(843-848)Online publication date: 4-Jul-2023
https://doi.org/10.1109/ICUFN57995.2023.10199942

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High Throughput Lung and Lobar Segmentation by 2D and 3D CNN on Chest CT with Diffuse Lung Disease

Lung CT Image Segmentation Using Deep Neural Networks

MRI Image Registration Considerably Improves CNN-Based Disease Classification

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