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Deep Learning-Based Differentiation between Mucinous Cystic Neoplasm and Serous Cystic Neoplasm in the Pancreas Using Endoscopic Ultrasonography

Title
Deep Learning-Based Differentiation between Mucinous Cystic Neoplasm and Serous Cystic Neoplasm in the Pancreas Using Endoscopic Ultrasonography
Authors
Nguon, Leang SimSeo, KangwonLim, Jung-HyunSong, Tae-JunCho, Sung-HyunPark, Jin-SeokPark, Suhyun
Ewha Authors
박수현
SCOPUS Author ID
박수현scopusscopus
Issue Date
2021
Journal Title
DIAGNOSTICS
ISSN
2075-4418JCR Link
Citation
DIAGNOSTICS vol. 11, no. 6
Keywords
endoscopic ultrasonographymucinous cystic neoplasmserous cystic neoplasmdeep learningconvolutional neural networkpancreatic cystic neoplasmsdiagnostic imaging
Publisher
MDPI
Indexed
SCIE; SCOPUS WOS
Document Type
Article
Abstract
Mucinous cystic neoplasms (MCN) and serous cystic neoplasms (SCN) account for a large portion of solitary pancreatic cystic neoplasms (PCN). In this study we implemented a convolutional neural network (CNN) model using ResNet50 to differentiate between MCN and SCN. The training data were collected retrospectively from 59 MCN and 49 SCN patients from two different hospitals. Data augmentation was used to enhance the size and quality of training datasets. Fine-tuning training approaches were utilized by adopting the pre-trained model from transfer learning while training selected layers. Testing of the network was conducted by varying the endoscopic ultrasonography (EUS) image sizes and positions to evaluate the network performance for differentiation. The proposed network model achieved up to 82.75% accuracy and a 0.88 (95% CI: 0.817-0.930) area under curve (AUC) score. The performance of the implemented deep learning networks in decision-making using only EUS images is comparable to that of traditional manual decision-making using EUS images along with supporting clinical information. Gradient-weighted class activation mapping (Grad-CAM) confirmed that the network model learned the features from the cyst region accurately. This study proves the feasibility of diagnosing MCN and SCN using a deep learning network model. Further improvement using more datasets is needed.
DOI
10.3390/diagnostics11061052
Appears in Collections:
ETC > ETC
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