Detection and Classification of Colorectal Polyp Using Deep Learning

Title

Detection and Classification of Colorectal Polyp Using Deep Learning

Creator

Tanwar S.; Vijayalakshmi S.; Sabharwal M.; Kaur M.; Alzubi A.A.; Lee H.-N.

Description

Colorectal Cancer (CRC) is the third most dangerous cancer in the world and also increasing day by day. So, timely and accurate diagnosis is required to save the life of patients. Cancer grows from polyps which can be either cancerous or noncancerous. So, if the cancerous polyps are detected accurately and removed on time, then the dangerous consequences of cancer can be reduced to a large extent. The colonoscopy is used to detect the presence of colorectal polyps. However, manual examinations performed by experts are prone to various errors. Therefore, some researchers have utilized machine and deep learning-based models to automate the diagnosis process. However, existing models suffer from overfitting and gradient vanishing problems. To overcome these problems, a convolutional neural network- (CNN-) based deep learning model is proposed. Initially, guided image filter and dynamic histogram equalization approaches are used to filter and enhance the colonoscopy images. Thereafter, Single Shot MultiBox Detector (SSD) is used to efficiently detect and classify colorectal polyps from colonoscopy images. Finally, fully connected layers with dropouts are used to classify the polyp classes. Extensive experimental results on benchmark dataset show that the proposed model achieves significantly better results than the competitive models. The proposed model can detect and classify colorectal polyps from the colonoscopy images with 92% accuracy. 2022 Sushama Tanwar et al.

Source

BioMed Research International, Vol-2022

Date

2022-01-01

Publisher

Hindawi Limited

Coverage

Tanwar S., Galgotias University, Uttar Pradesh, 201307, India; Vijayalakshmi S., Christ University, Lavasa, 412112, India; Sabharwal M., Galgotias University, Uttar Pradesh, 201307, India; Kaur M., Gwangju Institute of Science and Technology, School of Electrical Engineering and Computer Science, Gwangju, 61005, South Korea; Alzubi A.A., Computer Science Department, Community College, King Saud University, Riyadh, Saudi Arabia; Lee H.-N., Gwangju Institute of Science and Technology, School of Electrical Engineering and Computer Science, Gwangju, 61005, South Korea

Rights

All Open Access; Green Open Access; Hybrid Gold Open Access

Relation

ISSN: 23146133; PubMed ID: 35463989

Format

Online

Language

English

Type

Article

Identifier

https://doi.org/10.1155/2022/2805607

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85128802566&doi=10.1155%2f2022%2f2805607&partnerID=40&md5=1a782f1a4b7dafdef8ec8b29a227fab1