Detection and Classification of Colorectal Polyp Using Deep Learning. (15th April 2022)

• Record Type: Journal Article
• Authors: Tanwar, Sushama; Vijayalakshmi, S.; Sabharwal, Munish; Kaur, Manjit; AlZubi, Ahmad Ali; Lee, Heung-No
• Other Names: Parameshachari B. D. Academic Editor.
• Is Part Of: BioMed research international. Volume 2022(2022)
• Publication Date: 2022-04-15
• Subjects: Medicine -- Periodicals; Biology -- Periodicals; Biotechnology -- Periodicals; Life sciences -- Periodicals; 610.5
• DOI: 10.1155/2022/2805607
• Languages: English
• ISSNs: 2314-6133

Abstract

Abstract : 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.