Authors:
Insaf Setitra
1
;
Yuji Iwahori
2
;
Yacine Elhamer
1
;
Anais Mezrag
1
;
Shinji Fukui
3
and
Kunio Kasugai
4
Affiliations:
1
Department of Artificial Intelligence and Data Science, University of Science and Technology Houari Bouemediene, USTHB, Algiers, Algeria
;
2
Department of Computer Science, Chubu University, Kasugai, Aichi, 487-8501 Japan
;
3
Department of Information Education, Aichi University of Education, Kariya, Aichi, 448-0001 Japan
;
4
Department of Gastroenterology, Aichi Medical University, Nagakute, Aichi, 480-1195 Japan
Keyword(s):
Polyp Size Estimation, Polyp Segmentation, Blood Vessel, Colorectal Cancer, PVT, Autoencoder.
Abstract:
The size of colorectal polyps is one of the factors conditioning the risk of synchronous and metachronous
colorectal cancer (CRC). In this work, we are interested in the automatic measurement of polyp sizes in
colonoscopy videos. The study is performed in two steps: (1) first the detection and segmentation of the
polyp by the neural network Polyp-PVT and then (2) the classification of the polyp into different classes (type
of disease, size of the polyp). This is done by extracting information from blood vessels, a parameter that
has a low variability and is present in the majority of colonoscopic videos. This method has been validated
by two local Hepato-Gastro-Enterology specialists. Once the size of the polyp is extracted, a classification
of polyps as susceptible malignant (polyp size ≥ 6 mm) and susceptible benign (polyp size < 6 mm) is
performed. Our approach reaches an accuracy of 85.61% for the first category and 73.92% for the second
one and is comparable to human cl
assification which is estimated to 52% for beginners and 71% for experts
endoscopists.
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