Wireless Capsule Endoscopy: A New Tool for Cancer Screening in the Colon With Deep-Learning-Based Polyp Recognition

Xiao, Jia; Xing, Xiaohan; Yuan, Yixuan; Liu, Xing; Meng, Max Q.-H.

doi:10.1109/jproc.2019.2950506

Cited by 67 publications

(21 citation statements)

References 119 publications

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“…Wireless capsule endoscopy may even help with a tiny camera to capture images of a patient's internal colon [5]. A wireless capsule endoscopic camera is supported with a capsule the size of a vitamin to swallow.…”

Section: Wireless Capsule Endoscopymentioning

confidence: 99%

Intelligent recognition of colorectal cancer combining application of computer-assisted diagnosis with deep learning approaches

Raju

Jayavel²,

Rajalakshmi³

2022

IJECE

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<span>The malignancy of the colorectal testing methods has been exposed triumph to decrease the occurrence and death rate; this cancer is the relatively sluggish rising and has an extremely peculiar to develop the premalignant lesions. Now, many patients are not going to colorectal cancer screening, and people who do, are able to diagnose existing tests and screening methods. The most important concept of this motivation for this research idea is to evaluate the recognized data from the immediately available colorectal cancer screening methods. The data provided to laboratory technologists is important in the formulation of appropriate recommendations that will reduce colorectal cancer. With all standard colon cancer tests can be recognized agitatedly, the treatment of colorectal cancer is more efficient. The intelligent computer assisted diagnosis (CAD) is the most powerful technique for recognition of colorectal cancer in recent advances. It is a lot to reduce the level of interference nature has contributed considerably to the advancement of the quality of cancer treatment. To enhance diagnostic accuracy intelligent CAD has a research always active, ongoing with the deep learning and machine learning approaches with the associated convolutional neural network (CNN) scheme.</span>

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Section: Wireless Capsule Endoscopymentioning

confidence: 99%

Intelligent recognition of colorectal cancer combining application of computer-assisted diagnosis with deep learning approaches

Raju

Jayavel²,

Rajalakshmi³

2022

IJECE

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“…However, the traditional ML methods based on handcrafted features suffer from low performance [16]. The recent works on polyp segmentation using both video capsule endoscopy and colonoscopy mostly relied on Deep Neural Network (DNN) [17]- [23].…”

Section: A Semantic Segmentation Of Endoscopic Imagesmentioning

confidence: 99%

NanoNet: Real-Time Polyp Segmentation in Video Capsule Endoscopy and Colonoscopy

Jha¹,

Tomar²,

Ali³

et al. 2021

Preprint

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Deep learning in gastrointestinal endoscopy can assist to improve clinical performance and be helpful to assess lesions more accurately. To this extent, semantic segmentation methods that can perform automated real-time delineation of a region-of-interest, e.g., boundary identification of cancer or precancerous lesions, can benefit both diagnosis and interventions. However, accurate and real-time segmentation of endoscopic images is extremely challenging due to its high operator dependence and high-definition image quality. To utilize automated methods in clinical settings, it is crucial to design lightweight models with low latency such that they can be integrated with low-end endoscope hardware devices. In this work, we propose NanoNet, a novel architecture for the segmentation of video capsule endoscopy and colonoscopy images. Our proposed architecture allows real-time performance and has higher segmentation accuracy compared to other more complex ones. We use video capsule endoscopy and standard colonoscopy datasets with polyps, and a dataset consisting of endoscopy biopsies and surgical instruments, to evaluate the effectiveness of our approach. Our experiments demonstrate the increased performance of our architecture in terms of a trade-off between model complexity, speed, model parameters, and metric performances. Moreover, the resulting models size is relatively tiny, with only nearly 36,000 parameters compared to traditional deep learning approaches having millions of parameters.

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“…Recently, various deep learning methods have been applied to polyps detection with the revolution of computational technologies [5], [6], [8], [9]. Hierarchical feature learning and discrimination capabilities of neural networks can help significantly improve polyps detection accuracy.…”

Section: A Previous Work On Colon Imaging Datamentioning

confidence: 99%

Deep Learning-based Biological Anatomical Landmark Detection in Colonoscopy Videos

Kaiwei¹,

Ye²,

Ma³

et al. 2021

Preprint

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Colonoscopy is a standard imaging tool for visualizing the entire gastrointestinal (GI) tract of patients to capture lesion areas. However, it takes the clinicians excessive time to review a large number of images extracted from colonoscopy videos. Thus, automatic detection of biological anatomical landmarks within the colon is highly demanded, which can help reduce the burden of clinicians by providing guidance information for the locations of lesion areas. In this article, we propose a novel deep learning-based approach to detect biological anatomical landmarks in colonoscopy videos. First, raw colonoscopy video sequences are pre-processed to reject interference frames. Second, a ResNet-101 based network is used to detect three biological anatomical landmarks separately to obtain the intermediate detection results. Third, to achieve more reliable localization of the landmark periods within the whole video period, we propose to post-process the intermediate detection results by identifying the incorrectly predicted frames based on their temporal distribution and reassigning them back to the correct class. Finally, the average detection accuracy reaches 99.75%. Meanwhile, the average IoU of 0.91 shows a high degree of similarity between our predicted landmark periods and ground truth. The experimental results demonstrate that our proposed model is capable of accurately detecting and localizing biological anatomical landmarks from colonoscopy videos.

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Wireless Capsule Endoscopy: A New Tool for Cancer Screening in the Colon With Deep-Learning-Based Polyp Recognition

Cited by 67 publications

References 119 publications

Intelligent recognition of colorectal cancer combining application of computer-assisted diagnosis with deep learning approaches

Intelligent recognition of colorectal cancer combining application of computer-assisted diagnosis with deep learning approaches

NanoNet: Real-Time Polyp Segmentation in Video Capsule Endoscopy and Colonoscopy

Deep Learning-based Biological Anatomical Landmark Detection in Colonoscopy Videos

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