Assistance system for real-time polyp detection based on convolutional neural network

Summary: The use of artifi cial intelligence as an assistive detection method in endoscopy has attracted increasing interest in recent years. Machine learning algorithms promise to improve the effi ciency of polyp detection and even optical localization of fi ndings, all with minimal training of the endoscopist. The practical goal of this study is to analyse the CAD software (computer-aided dia gnosis) Carebot for colorectal polyp detection using a convolutional neural network. The proposed binary classifier for polyp detection achieves accuracy of up to 98%, specifi city of 0.99 and precision of 0.96. At the same time, the need for the availability of large-scale clinical data for the development of artifi cial- -intelligence-based models for the automatic detection of adenomas and benign neoplastic lesions is discussed. Key words: polyp detection – convolutional neural network – artifi cial intelligence – computer-aided dia gnosis – spatial location

Mobile-PolypNet : Light-weight Colon Polyp Segmentation Network for Low Resources Settings

Colon polyps, small clump of cells on the lining of the colon can lead to Colorectal cancer (CRC), one of the leading types of cancer globally. Hence, early detection of these polyps is crucial in the prevention of CRC. This paper proposes a lightweight deep learning model for colorectal polyp segmentation that achieved state-of-the-art accuracy while significantly reducing the model size and complexity. The proposed deep learning autoencoder model employs a set of state-of-the-art architectural blocks and optimization objective functions to achieve the desired efficiency. The model is trained and tested on five publicly available colorectal polyp segmentation datasets (CVC-ClinicDB, CVC-ColonDB, EndoScene, Kvasir, and ETIS). We also performed ablation testing on the model to test various aspects of the autoencoder architecture. We performed the model evaluation using most of the common image segmentation metrics. The backbone model achieved a dice score of 0.935 on the Kvasir dataset and 0.945 on the CVC-ClinicDB dataset improving the accuracy by 4.12% and 5.12% respectively over the current state-of-the-art network, while using 88 times fewer parameters, 40 times less storage space, and being computationally 17 times more efficient. Our ablation study showed that the addition of ConvSkip in the autoencoder slightly improves the model’s performance but it was not significant (p-value=0.815).

An artificial intelligence-based quality improvement system significantly improved the efficacy of computer-aided detection system in colonoscopy: A four group parallel study

Background and study aims: Tandem colonoscopy studies have found that about one in five adenomas are missed at colonoscopy. It is still debatable whether the combination of a computer-aided detection (CADe) system for colorectal polyp detection with a computer-aided quality improvement (CAQ) system for real-time withdrawal speed monitoring may result in additional benefits in the task of adenoma detection or if the synergetic effect may be harmed due to excessive visual burden resulting from the information overload. This study aims to evaluate the interaction effect on improving the adenoma detection rate (ADR). Patients and methods: This is a single-center, randomized, four-group parallel controlled study, performed in Renmin Hospital of Wuhan University. Between July 1, 2020 and Oct 15, 2020, 1076 participants were randomly allocated into four treatment groups [control: 271, CADe: 268, CAQ: 269 and CADe plus CAQ (COMBO): 268]. The primary outcome was the ADR. Results: The average ADR in the control, CADe, CAQ and COMBO groups was 14.76% (95% C.I. 10.54-18.98), 21.27% (95% C.I. 16.37-26.17), 24.54% (95% C.I. 19.39-29.68) and 30.6% (95% C.I. 25.08-36.11), respectively. The ADR was higher in the COMBO group compared with the CADe group but not compared with the CAQ group (21.27% VS 30.6%, P=0.024, OR 1.284, 95%C.I. 1.033-1.596; 24.54%vs. 30.6%, P = 0.213, OR = 1.309, 95% C.I. 0.857-2, respectively). Conclusions: CAQ significantly improved the efficacy of CADe in a four-group parallel controlled study. No significant difference in the ADR or PDR was found between the CAQ and COMBO groups.

SRGAN Assisted Encoder-Decoder Deep Neural Network for Colorectal Polyp Semantic Segmentation

Colon cancer is thought about as the third most regularly identified cancer after Brest and lung cancer. Most colon cancers are adenocarcinomas developing from adenomatous polyps, grow on the intima of the colon. The standard procedure for polyp detection is colonoscopy, where the success of the standard colonoscopy depends on the colonoscopist experience and other environmental factors. Nonetheless, throughout colonoscopy procedures, a considerable number (8-37%) of polyps are missed due to human mistakes, and these missed polyps are the prospective reason for colorectal cancer cells. In the last few years, many research groups developed deep learning-based computer-aided (CAD) systems that recommended many techniques for automated polyp detection, localization, and segmentation. Still, accurate polyp detection, segmentation is required to minimize polyp miss out rates. This paper suggested a Super-Resolution Generative Adversarial Network (SRGAN) assisted Encoder-Decoder network for fully automated colon polyp segmentation from colonoscopic images. The proposed deep learning model incorporates the SRGAN in the up-sampling process to achieve more accurate polyp segmentation. We examined our model on the publicly available benchmark datasets CVC-ColonDB and Warwick- QU. The model accomplished a dice score of 0.948 on the CVC-ColonDB dataset, surpassed the recently advanced state-of-the-art (SOTA) techniques. When it is evaluated on the Warwick-QU dataset, it attains a Dice Score of 0.936 on part A and 0.895 on Part B. Our model showed more accurate results for sessile and smaller-sized polyps.