research-article

Comparison Study of Deep Learning Models for Colorectal Lesions Classification

Authors:

Berk Cetinsaya,

Douglas RexAuthors Info & Claims

ICISDM '20: Proceedings of the 2020 the 4th International Conference on Information System and Data Mining

Pages 84 - 88

https://doi.org/10.1145/3404663.3404667

Published: 10 July 2020 Publication History

Abstract

In this paper, we performed a comparison study between GoogLeNet, AlexNet, and InceptionV3 deep learning models to recognize and classify colorectal cancer tumors. Colorectal tumors are one of the very common cancer types and early detection could result in a significantly higher survival rate of 95% as opposed to 12%. In this work, we aim to investigate the deep learning models to automatically detect the tumor types from polyp images. We, therefore, used actual images taken from the colorectal surgery or colonoscopy using Narrow-band imaging (NBI). The images are classified based on NBI International Colorectal Endoscopic (NICE) classification. We used NICE 1 and NICE 2 types with a total of 2604 images in the size of 64x64. Our results show that the InceptionV3 model has the most accurate results by average 92.39% where AlexNet is 88.19% and GoogLeNet is 85.73%.

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Cited By

Dials JDemirel DSanchez-Arias RHalic TDe SGromski M(2024)Endoscopic sleeve gastroplasty: stomach location and task classification for evaluation using artificial intelligenceInternational Journal of Computer Assisted Radiology and Surgery10.1007/s11548-023-03054-219:4(635-644)Online publication date: 11-Jan-2024
https://doi.org/10.1007/s11548-023-03054-2
Mohapatra SPati GSwarnkar T(2022)Efficiency of Transfer Learning for Abnormality Detection using Colonoscopy Images: A Critical Analysis2022 IEEE Fourth International Conference on Advances in Electronics, Computers and Communications (ICAECC)10.1109/ICAECC54045.2022.9716610(1-6)Online publication date: 10-Jan-2022
https://doi.org/10.1109/ICAECC54045.2022.9716610

Index Terms

Comparison Study of Deep Learning Models for Colorectal Lesions Classification
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks

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cover image ACM Other conferences

ICISDM '20: Proceedings of the 2020 the 4th International Conference on Information System and Data Mining

May 2020

170 pages

ISBN:9781450377652

DOI:10.1145/3404663

Copyright © 2020 ACM.

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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Publication History

Published: 10 July 2020

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NIH/NIGMS
NIH/NIBIB
NIAMS
NIH/NHLBI
NIH/NCI

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ICISDM 2020

ICISDM 2020: 2020 the 4th International Conference on Information System and Data Mining

May 15 - 17, 2020

HI, Hawaii, USA

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Cited By

Dials JDemirel DSanchez-Arias RHalic TDe SGromski M(2024)Endoscopic sleeve gastroplasty: stomach location and task classification for evaluation using artificial intelligenceInternational Journal of Computer Assisted Radiology and Surgery10.1007/s11548-023-03054-219:4(635-644)Online publication date: 11-Jan-2024
https://doi.org/10.1007/s11548-023-03054-2
Mohapatra SPati GSwarnkar T(2022)Efficiency of Transfer Learning for Abnormality Detection using Colonoscopy Images: A Critical Analysis2022 IEEE Fourth International Conference on Advances in Electronics, Computers and Communications (ICAECC)10.1109/ICAECC54045.2022.9716610(1-6)Online publication date: 10-Jan-2022
https://doi.org/10.1109/ICAECC54045.2022.9716610

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