Computer-Aided Colon Polyp Detection on High Resolution Colonoscopy Using Transfer Learning Techniques

Colonoscopies reduce the incidence of colorectal cancer through early recognition and resecting of the colon polyps. However, the colon polyp miss detection rate is as high as 26% in conventional colonoscopy. The search for methods to decrease the polyp miss rate is nowadays a paramount task. A number of algorithms or systems have been developed to enhance polyp detection, but few are suitable for real-time detection or classification due to their limited computational ability. Recent studies indicate that the automated colon polyp detection system is developing at an astonishing speed. Real-time detection with classification is still a yet to be explored field. Newer image pattern recognition algorithms with convolutional neuro-network (CNN) transfer learning has shed light on this topic. We proposed a study using real-time colonoscopies with the CNN transfer learning approach. Several multi-class classifiers were trained and mAP ranged from 38% to 49%. Based on an Inception v2 model, a detector adopting a Faster R-CNN was trained. The mAP of the detector was 77%, which was an improvement of 35% compared to the same type of multi-class classifier. Therefore, our results indicated that the polyp detection model could attain a high accuracy, but the polyp type classification still leaves room for improvement.

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Artificial Intelligence-Assisted Colonoscopy for Detection of Colon Polyps: a Prospective, Randomized Cohort Study

Journal of Gastrointestinal Surgery ◽

10.1007/s11605-020-04802-4 ◽

2020 ◽

Author(s):

Yuchen Luo ◽

Yi Zhang ◽

Ming Liu ◽

Yihong Lai ◽

Panpan Liu ◽

...

Keyword(s):

Artificial Intelligence ◽

Real Time ◽

High Performance ◽

Detection System ◽

Random Order ◽

Colon Polyps ◽

Clinical Environment ◽

Polyp Detection ◽

Link Type ◽

Polyp Detection Rate

Abstract Background and aims Improving the rate of polyp detection is an important measure to prevent colorectal cancer (CRC). Real-time automatic polyp detection systems, through deep learning methods, can learn and perform specific endoscopic tasks previously performed by endoscopists. The purpose of this study was to explore whether a high-performance, real-time automatic polyp detection system could improve the polyp detection rate (PDR) in the actual clinical environment. Methods The selected patients underwent same-day, back-to-back colonoscopies in a random order, with either traditional colonoscopy or artificial intelligence (AI)-assisted colonoscopy performed first by different experienced endoscopists (> 3000 colonoscopies). The primary outcome was the PDR. It was registered with clinicaltrials.gov. (NCT047126265). Results In this study, we randomized 150 patients. The AI system significantly increased the PDR (34.0% vs 38.7%, p < 0.001). In addition, AI-assisted colonoscopy increased the detection of polyps smaller than 6 mm (69 vs 91, p < 0.001), but no difference was found with regard to larger lesions. Conclusions A real-time automatic polyp detection system can increase the PDR, primarily for diminutive polyps. However, a larger sample size is still needed in the follow-up study to further verify this conclusion. Trial Registration clinicaltrials.gov Identifier: NCT047126265

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Real-Time Detection of Ground Objects Based on Unmanned Aerial Vehicle Remote Sensing with Deep Learning: Application in Excavator Detection for Pipeline Safety

Remote Sensing ◽

10.3390/rs12010182 ◽

2020 ◽

Vol 12 (1) ◽

pp. 182 ◽

Cited By ~ 10

Author(s):

Lingxuan Meng ◽

Zhixing Peng ◽

Ji Zhou ◽

Jirong Zhang ◽

Zhenyu Lu ◽

...

Keyword(s):

Remote Sensing ◽

Deep Learning ◽

Object Detection ◽

Real Time ◽

Unmanned Aerial Vehicle ◽

Detection System ◽

Detection Model ◽

Aerial Vehicle ◽

Real Time Detection ◽

Pipeline Safety

Unmanned aerial vehicle (UAV) remote sensing and deep learning provide a practical approach to object detection. However, most of the current approaches for processing UAV remote-sensing data cannot carry out object detection in real time for emergencies, such as firefighting. This study proposes a new approach for integrating UAV remote sensing and deep learning for the real-time detection of ground objects. Excavators, which usually threaten pipeline safety, are selected as the target object. A widely used deep-learning algorithm, namely You Only Look Once V3, is first used to train the excavator detection model on a workstation and then deployed on an embedded board that is carried by a UAV. The recall rate of the trained excavator detection model is 99.4%, demonstrating that the trained model has a very high accuracy. Then, the UAV for an excavator detection system (UAV-ED) is further constructed for operational application. UAV-ED is composed of a UAV Control Module, a UAV Module, and a Warning Module. A UAV experiment with different scenarios was conducted to evaluate the performance of the UAV-ED. The whole process from the UAV observation of an excavator to the Warning Module (350 km away from the testing area) receiving the detection results only lasted about 1.15 s. Thus, the UAV-ED system has good performance and would benefit the management of pipeline safety.

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Real-Time Detection on SPAD Value of Potato Plant Using an In-Field Spectral Imaging Sensor System

Sensors ◽

10.3390/s20123430 ◽

2020 ◽

Vol 20 (12) ◽

pp. 3430 ◽

Cited By ~ 2

Author(s):

Ning Liu ◽

Gang Liu ◽

Hong Sun

Keyword(s):

Real Time ◽

Potato Plant ◽

Vegetation Index ◽

Detection System ◽

Automatic Segmentation ◽

The Real ◽

Detection Model ◽

Potato Plants ◽

Spad Value ◽

Real Time Detection

In this study, a SPAD value detection system was developed based on a 25-wavelength spectral sensor to give a real-time indication of the nutrition distribution of potato plants in the field. Two major advantages of the detection system include the automatic segmentation of spectral images and the real-time detection of SPAD value, a recommended indicating parameter of chlorophyll content. The modified difference vegetation index (MDVI) linking the Otsu algorithm (OTSU) and the connected domain-labeling (CDL) method (MDVI–OTSU–CDL) is proposed to accurately extract the potato plant. Additionally, the segmentation accuracy under different modified coefficients of MDVI was analyzed. Then, the reflectance of potato plants was extracted by the segmented mask images. The partial least squares (PLS) regression was employed to establish the SPAD value detection model based on sensitive variables selected using the uninformative variable elimination (UVE) algorithm. Based on the segmented spectral image and the UVE–PLS model, the visualization distribution map of SPAD value was drawn by pseudo-color processing technology. Finally, the testing dataset was employed to measure the stability and practicality of the developed detection system. This study provides a powerful support for the real-time detection of SPAD value and the distribution of crops in the field.

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Real-time Detection of Aortic Valve in Echocardiography using Convolutional Neural Networks

Current Medical Imaging Formerly Current Medical Imaging Reviews ◽

10.2174/1573405615666190114151255 ◽

2020 ◽

Vol 16 (5) ◽

pp. 584-591 ◽

Cited By ~ 1

Author(s):

Muhammad Hanif Ahmad Nizar ◽

Chow Khuen Chan ◽

Azira Khalil ◽

Ahmad Khairuddin Mohamed Yusof ◽

Khin Wee Lai

Keyword(s):

Neural Network ◽

Neural Networks ◽

Heart Disease ◽

Aortic Valve ◽

Real Time ◽

Convolutional Neural Networks ◽

Valvular Heart Disease ◽

Detection System ◽

Processing Unit ◽

Real Time Detection

Background: Valvular heart disease is a serious disease leading to mortality and increasing medical care cost. The aortic valve is the most common valve affected by this disease. Doctors rely on echocardiogram for diagnosing and evaluating valvular heart disease. However, the images from echocardiogram are poor in comparison to Computerized Tomography and Magnetic Resonance Imaging scan. This study proposes the development of Convolutional Neural Networks (CNN) that can function optimally during a live echocardiographic examination for detection of the aortic valve. An automated detection system in an echocardiogram will improve the accuracy of medical diagnosis and can provide further medical analysis from the resulting detection. Methods: Two detection architectures, Single Shot Multibox Detector (SSD) and Faster Regional based Convolutional Neural Network (R-CNN) with various feature extractors were trained on echocardiography images from 33 patients. Thereafter, the models were tested on 10 echocardiography videos. Results: Faster R-CNN Inception v2 had shown the highest accuracy (98.6%) followed closely by SSD Mobilenet v2. In terms of speed, SSD Mobilenet v2 resulted in a loss of 46.81% in framesper- second (fps) during real-time detection but managed to perform better than the other neural network models. Additionally, SSD Mobilenet v2 used the least amount of Graphic Processing Unit (GPU) but the Central Processing Unit (CPU) usage was relatively similar throughout all models. Conclusion: Our findings provide a foundation for implementing a convolutional detection system to echocardiography for medical purposes.

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Real-time Detection System of Electrical Disturbances for Remote Communication Stations and Smart Grid

IEEE Latin America Transactions ◽

10.1109/tla.2017.8071233 ◽

2017 ◽

Vol 15 (10) ◽

pp. 1894-1900

Author(s):

Wesley Natanael Gallo ◽

Tales Heimfarth ◽

Danton Diego Ferreira ◽

Thais Martins Mendes

Keyword(s):

Smart Grid ◽

Real Time ◽

Detection System ◽

Remote Communication ◽

Real Time Detection

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An Automated Real-Time Face Mask Detection System Using Transfer Learning with Faster-RCNN in the Era of the COVID-19 Pandemic

Computers Materials & Continua ◽

10.32604/cmc.2022.017865 ◽

2022 ◽

Vol 71 (2) ◽

pp. 4151-4166

Author(s):

Maha Farouk S. Sabir ◽

Irfan Mehmood ◽

Wafaa Adnan Alsaggaf ◽

Enas Fawai Khairullah ◽

Samar Alhuraiji ◽

...

Keyword(s):

Real Time ◽

Transfer Learning ◽

Detection System ◽

Face Mask

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Real-time detection system of cat-eye effect target based on embedded platform

High Power Laser and Particle Beams ◽

10.3788/hplpb20152701.11013 ◽

2015 ◽

Vol 27 (1) ◽

pp. 11013

Author(s):

韩磊 HanLei ◽

张海洋 Zhang Haiyang ◽

马雪松 Ma Xuesong ◽

赵长明 Zhao Changming ◽

杨苏辉 Yang Suhui

Keyword(s):

Real Time ◽

Detection System ◽

Embedded Platform ◽

Real Time Detection

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Robust Real-Time Detection of Laparoscopic Instruments in Robot Surgery Using Convolutional Neural Networks with Motion Vector Prediction

Applied Sciences ◽

10.3390/app9142865 ◽

2019 ◽

Vol 9 (14) ◽

pp. 2865 ◽

Cited By ~ 4

Author(s):

Kyungmin Jo ◽

Yuna Choi ◽

Jaesoon Choi ◽

Jong Woo Chung

Keyword(s):

Neural Networks ◽

Real Time ◽

Convolutional Neural Networks ◽

Detection System ◽

Motion Vector ◽

Detection Algorithm ◽

Surgical Instruments ◽

The Mean ◽

Real Time Detection ◽

Motion Vector Prediction

More than half of post-operative complications can be prevented, and operation performances can be improved based on the feedback gathered from operations or notifications of the risks during operations in real time. However, existing surgical analysis methods are limited, because they involve time-consuming processes and subjective opinions. Therefore, the detection of surgical instruments is necessary for (a) conducting objective analyses, or (b) providing risk notifications associated with a surgical procedure in real time. We propose a new real-time detection algorithm for detection of surgical instruments using convolutional neural networks (CNNs). This algorithm is based on an object detection system YOLO9000 and ensures continuity of detection of the surgical tools in successive imaging frames based on motion vector prediction. This method exhibits a constant performance irrespective of a surgical instrument class, while the mean average precision (mAP) of all the tools is 84.7, with a speed of 38 frames per second (FPS).

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