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Classification of Pancreatic Tumors based on MRI Images using 3D Convolutional Neural Networks

Authors:

Xuan Chen,

Yufei Chen,

Chao Ma,

Xianhui Liu,

Xin TangAuthors Info & Claims

ISICDM 2018: Proceedings of the 2nd International Symposium on Image Computing and Digital Medicine

Pages 92 - 96

https://doi.org/10.1145/3285996.3286017

Published: 13 October 2018 Publication History

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Abstract

Computer aided diagnosis of pancreatic cancer can help doctors improve diagnostic efficiency and accuracy, which does not depend on the doctor's subjective judgment and experience. Existing pancreatic tumors classification methods suffer from the problem of partial automation and ignore spatial and temporal characteristics. In this paper, we used 3D versions of ResNet18, ResNet34, ResNet52 and Inception-ResNet for pancreatic magnetic resonance images (MRI) classification. In order to alleviate the effect of class imbalance, we proposed a weighted loss function. Two sets of comparative experiments were performed to compare the effect of the proposed loss function. Experimental results show with weighted loss function, the performances of the four models are mostly improved except the precision of ResNet52. Moreover, the false negatives are reduced. Among them, ResNet18 performs best and achieves the accuracy of 91%.

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Cited By

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Liu WZhang BLiu TJiang JLiu Y(2024)Artificial Intelligence in Pancreatic Image Analysis: A ReviewSensors10.3390/s2414474924:14(4749)Online publication date: 22-Jul-2024
https://doi.org/10.3390/s24144749
Qu JXiao XWei XQian X(2024)A causality-inspired generalized model for automated pancreatic cancer diagnosisMedical Image Analysis10.1016/j.media.2024.10315494(103154)Online publication date: May-2024
https://doi.org/10.1016/j.media.2024.103154
Faur ALazar DGhenciu L(2023)Artificial intelligence as a noninvasive tool for pancreatic cancer prediction and diagnosisWorld Journal of Gastroenterology10.3748/wjg.v29.i12.181129:12(1811-1823)Online publication date: 28-Mar-2023
https://doi.org/10.3748/wjg.v29.i12.1811
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Index Terms

Classification of Pancreatic Tumors based on MRI Images using 3D Convolutional Neural Networks
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Image and video acquisition
        3D imaging

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ISICDM 2018: Proceedings of the 2nd International Symposium on Image Computing and Digital Medicine

October 2018

166 pages

ISBN:9781450365338

DOI:10.1145/3285996

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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University of Electronic Science and Technology of China: University of Electronic Science and Technology of China

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Association for Computing Machinery

New York, NY, United States

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Published: 13 October 2018

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the National Natural Science Foundation of China
the Shanghai Innovation Action Project of Science and Technology

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ISICDM 2018

ISICDM 2018: The 2nd International Symposium on Image Computing and Digital Medicine 2018

October 13 - 14, 2018

Chengdu, China

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Liu WZhang BLiu TJiang JLiu Y(2024)Artificial Intelligence in Pancreatic Image Analysis: A ReviewSensors10.3390/s2414474924:14(4749)Online publication date: 22-Jul-2024
https://doi.org/10.3390/s24144749
Qu JXiao XWei XQian X(2024)A causality-inspired generalized model for automated pancreatic cancer diagnosisMedical Image Analysis10.1016/j.media.2024.10315494(103154)Online publication date: May-2024
https://doi.org/10.1016/j.media.2024.103154
Faur ALazar DGhenciu L(2023)Artificial intelligence as a noninvasive tool for pancreatic cancer prediction and diagnosisWorld Journal of Gastroenterology10.3748/wjg.v29.i12.181129:12(1811-1823)Online publication date: 28-Mar-2023
https://doi.org/10.3748/wjg.v29.i12.1811
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
Lamba KRani S(2023)Deep Learning based Analysis for Automated Detection and Classification of Brain Tumor2023 4th International Conference on Electronics and Sustainable Communication Systems (ICESC)10.1109/ICESC57686.2023.10193233(1760-1765)Online publication date: 6-Jul-2023
https://doi.org/10.1109/ICESC57686.2023.10193233
Lamba KRani S(2023)Deep Learning-Based Automated Detection and Classification of Brain Tumor with VGG16-SVM in Internet of HealthcareSN Computer Science10.1007/s42979-023-02446-05:1Online publication date: 14-Dec-2023
https://doi.org/10.1007/s42979-023-02446-0
Arabahmadi MFarahbakhsh RRezazadeh J(2022)Deep Learning for Smart Healthcare—A Survey on Brain Tumor Detection from Medical ImagingSensors10.3390/s2205196022:5(1960)Online publication date: 2-Mar-2022
https://doi.org/10.3390/s22051960
Wang ZSun YShen QCao L(2019)Dilated 3D Convolutional Neural Networks for Brain MRI Data ClassificationIEEE Access10.1109/ACCESS.2019.29419127(134388-134398)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2941912
Liu FZhou YFishman EYuille A(2019)FusionNet: Incorporating Shape and Texture for Abnormality Detection in 3D Abdominal CT ScansMachine Learning in Medical Imaging10.1007/978-3-030-32692-0_26(221-229)Online publication date: 10-Oct-2019
https://doi.org/10.1007/978-3-030-32692-0_26

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Breast tumor detection and classification based on density

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