Colorectal Disease Classification Using Efficiently Scaled Dilation in Convolutional Neural Network
- Authors
- Poudel S.; Kim Y.J.; Vo D.M.; Lee S.-W.
- Issue Date
- May-2020
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Keywords
- colon disease classification; colon disease classification with CNN; Colorectal image classification
- Citation
- IEEE Access, v.8, pp.99227 - 99238
- Journal Title
- IEEE Access
- Volume
- 8
- Start Page
- 99227
- End Page
- 99238
- URI
- https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/64617
- DOI
- 10.1109/ACCESS.2020.2996770
- ISSN
- 2169-3536
- Abstract
- Computer-aided diagnosis systems developed by computer vision researchers have helped doctors to recognize several endoscopic colorectal diseases more rapidly, which allows appropriate treatment and increases the patient's survival ratio. Herein, we present a robust architecture for endoscopic image classification using an efficient dilation in Convolutional Neural Network (CNNs). It has a high receptive field of view at the deep layers in increasing and decreasing dilation factor to preserve spatial details. We argue that dimensionality reduction in CNN can cause the loss of spatial information, resulting in miss of polyps and confusion in similar-looking images. Additionally, we use a regularization technique called DropBlock to reduce overfitting and deal with noise and artifacts. We compare and evaluate our method using various metrics: accuracy, recall, precision, and F1-score. Our experiments demonstrate that the proposed method provides the F1-score of 0.93 for Colorectal dataset and F1-score of 0.88 for KVASIR dataset. Experiments show higher accuracy of the proposed method over traditional methods when classifying endoscopic colon diseases. © 2013 IEEE.
- Files in This Item
- There are no files associated with this item.
- Appears in
Collections - IT융합대학 > 소프트웨어학과 > 1. Journal Articles
- 의과대학 > 의학과 > 1. Journal Articles
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.