Combined Feature Learning And CNN For Polyp Detection In Wireless Capsule Endoscopy Images

Combined Feature Learning And CNN For Polyp Detection In Wireless Capsule Endoscopy Images

© 2021 by IJETT Journal
Volume-69 Issue-6
Year of Publication : 2021
Authors : S.Sunitha, Dr.S.S. Sujatha
DOI :  10.14445/22315381/IJETT-V69I6P230

How to Cite?

S.Sunitha, Dr.S.S. Sujatha, "Combined Feature Learning And CNN For Polyp Detection In Wireless Capsule Endoscopy Images," International Journal of Engineering Trends and Technology, vol. 69, no. 6, pp. 206-215, 2021. Crossref,

There is a high probability for the polyps of the small intestine to turn into a malignant tumor. So, it is mandatory to identify these polyps in the early stage and remove them. This would increase the patient`s survival rate many times. With the tremendous growth of the technology, Wireless Capsule Endoscopy (WCE) is viewed as an achievement in the medical field. WCE makes it easy, painless, and inexpensive to view an internal body that humans cannot see. However, the primary disadvantage of wireless capsule endoscopy is the poor image quality. Additionally, the shape, color, and texture of the human gastrointestinal (GI) tract resemble polyps. Hence, certain types of polyps cannot be identified even by a well-trained doctor. For this reason, a computer-aided polyp segmentation remains a problem to be solved. In this proposed research, the Combined Feature Learning (CFL) and Convolutional Neural Network (CNN) has been used to develop an automated polyp detection system. WCE images have been augmented to improve the training efficiency of the proposed CFL-CNN model. Furthermore, high and low level feature learning has been used in this proposed method to reduce the False Positive (FP) rate. The experimental results demonstrate that high-level features and image augmentation significantly reduce the FP intensity. This shows a significant improvement in overall performance in terms of precision, sensitivity, specificity, and recall. Additionally, the experiment results indicate that training the proposed CFL-CNN model requires a small amount of time and computational resources.

Wireless Capsule Endoscopy, Image Segmentation, Image Pre-Processing, CNN, Polyp Detection, Deep Learning.

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