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Article

A CNN CADx System for Multimodal Classification of Colorectal Polyps Combining WL, BLI, and LCI Modalities

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Department of Electrical Engineering, Video Coding and Architectures (VCA), Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands
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Division of Gastroenterology and Hepatology, Maastricht University Medical Center, 6229 HX Maastricht, The Netherlands
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School for Oncology and Developmental Biology (GROW), Maastricht University, 6229 ER Maastricht, The Netherlands
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Department of Gastroenterology and Hepatology, Catharina Hospital, 5623 EJ Eindhoven, The Netherlands
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School of Nutrition & Translational Research in Metabolism (NUTRIM), Maastricht University, 6229 ER Maastricht, The Netherlands
*
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(15), 5040; https://doi.org/10.3390/app10155040
Received: 29 April 2020 / Revised: 17 July 2020 / Accepted: 20 July 2020 / Published: 22 July 2020
(This article belongs to the Special Issue Medical Artificial Intelligence)
Colorectal polyps are critical indicators of colorectal cancer (CRC). Blue Laser Imaging and Linked Color Imaging are two modalities that allow improved visualization of the colon. In conjunction with the Blue Laser Imaging (BLI) Adenoma Serrated International Classification (BASIC) classification, endoscopists are capable of distinguishing benign and pre-malignant polyps. Despite these advancements, this classification still prevails a high misclassification rate for pre-malignant colorectal polyps. This work proposes a computer aided diagnosis (CADx) system that exploits the additional information contained in two novel imaging modalities, enabling more informative decision-making during colonoscopy. We train and benchmark six commonly used CNN architectures and compare the results with 19 endoscopists that employed the standard clinical classification model (BASIC). The proposed CADx system for classifying colorectal polyps achieves an area under the curve (AUC) of 0.97. Furthermore, we incorporate visual explanatory information together with a probability score, jointly computed from White Light, Blue Laser Imaging, and Linked Color Imaging. Our CADx system for automatic polyp malignancy classification facilitates future advances towards patient safety and may reduce time-consuming and costly histology assessment. View Full-Text
Keywords: blue light imaging; linked color imaging; colorectal polyp classification; artificial intelligence; deep learning; CADx; CNN blue light imaging; linked color imaging; colorectal polyp classification; artificial intelligence; deep learning; CADx; CNN
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MDPI and ACS Style

Fonollà, R.; E. W. van der Zander, Q.; Schreuder, R.M.; Masclee, A.A.M.; Schoon, E.J.; van der Sommen, F.; de With, P.H.N. A CNN CADx System for Multimodal Classification of Colorectal Polyps Combining WL, BLI, and LCI Modalities. Appl. Sci. 2020, 10, 5040. https://doi.org/10.3390/app10155040

AMA Style

Fonollà R, E. W. van der Zander Q, Schreuder RM, Masclee AAM, Schoon EJ, van der Sommen F, de With PHN. A CNN CADx System for Multimodal Classification of Colorectal Polyps Combining WL, BLI, and LCI Modalities. Applied Sciences. 2020; 10(15):5040. https://doi.org/10.3390/app10155040

Chicago/Turabian Style

Fonollà, Roger, Quirine E. W. van der Zander, Ramon M. Schreuder, Ad A.M. Masclee, Erik J. Schoon, Fons van der Sommen, and Peter H.N. de With 2020. "A CNN CADx System for Multimodal Classification of Colorectal Polyps Combining WL, BLI, and LCI Modalities" Applied Sciences 10, no. 15: 5040. https://doi.org/10.3390/app10155040

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