A New Approach to Polyp Detection by Pre-Processing of Images and Enhanced Faster R-CNN

Qian, Zhiqin; Lv, Yi; Lv, Dongyuan; Gu, Huijun; Wang, Kunyu; Zhang, Wenjun; Gupta, Madan M.

doi:10.1109/jsen.2020.3036005

Cited by 33 publications

(4 citation statements)

References 25 publications

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“…Qian et al [22] also presented an enhanced FasterRCNNbased system used for polyp detection by using preprocessed data. Specular reflections are one of the causes of false detection of polyps.…”

Section: Related Workmentioning

confidence: 99%

Colorectal Polyp Detection and Comparative Evaluation Based on Deep Learning Approaches

Chen,

Ahmad

2023

IEEE Access

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Colorectal cancer has been one of the leading causes of mortality over the past decade, and colorectal polyps are the leading cause of this disease. Conventional polyp detection techniques are insufficient for proper detection; thus, an efficient detection method is indispensable. In this study, we collected colorectal images from a hospital in Taiwan, annotated the ground truth of polyp locations, and integrated them with a public dataset to create a colonoscopy dataset. Data augmentation techniques are further used to increase the training dataset's diversity to improve the models' detection performance. By developing the comparison system based on the recent state-of-the-art methods (i.e., FasterRCNN, SSD, YOLOv3, and YOLOv4), we compared the measurement metrics and statistically analyzed the performance of the models to identify the significant statistical difference in models' performance. Moreover, we developed and integrated error handling mechanism with each model to discard the false and null predictions. Finally, our model comparison system selects and proposes the best performing deep learning model to detect and classify colorectal polyps. We expect that the proposed model will accurately locate and classify different types of polyps. Eventually, this approach will ensure a valuable medical aid model. INDEX TERMSColorectal cancer (CRC), colorectal polyps, polyp detection, deep learning, data augmentation, error handling.

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Section: Related Workmentioning

confidence: 99%

Colorectal Polyp Detection and Comparative Evaluation Based on Deep Learning Approaches

Chen,

Ahmad

2023

IEEE Access

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“…To date, several studies in the computer vision field explored Faster R-CNN in many different applications such as vehicle detection [6], disease detection [7], [8], face detection [9], [10], ship detection [11], [12], metal object detection [13], radar images [14], defect detection [15], [16], object detection on medical images [17], [18], and autonomous driving [19]. Although many researchers successfully implemented Faster R-CNN in object detection, there are few studies [20] that explored this detector for X-ray images due to limited data available and complicated procedures in collecting Xray images.…”

Section: Article Historymentioning

confidence: 99%

Identifying threat objects using faster region-based convolutional neural networks (faster R-CNN)

Galvez

Dadios²

2022

Int. J. Adv. Intell. Informatics

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Automated detection of threat objects in a security X-ray image is vital to prevent unwanted incidents in busy places like airports, train stations, and malls. The manual method of threat object detection is time-consuming and tedious. Also, the person on duty can overlook the threat objects due to limited time in checking every person’s belongings. As a solution, this paper presents a faster region-based convolutional neural network (Faster R-CNN) object detector to automatically identify threat objects in an X-ray image using the IEDXray dataset. The dataset was composed of scanned X-ray images of improvised explosive device (IED) replicas without the main charge. This paper extensively evaluates the Faster R-CNN architecture in threat object detection to determine which configuration can be used to improve the detection performance. Our findings showed that the proposed method could identify three classes of threat objects in X-ray images. In addition, the mean average precision (mAP) of the threat object detector could be improved by increasing the input image's image resolution but sacrificing the detector's speed. The threat object detector achieved 77.59% mAP and recorded an inference time of 208.96 ms by resizing the input image to 900 × 1536 resolution. Results also showed that increasing the bounding box proposals did not significantly improve the detection performance. The mAP using 150 bounding box proposals only achieved 75.65% mAP, and increasing the bounding box proposal twice reduced the mAP to 72.22%.

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“…The objective of this paper is to a train an agent (i.e., prosthetic hand) using three different algorithms-Soft Actor-Critic (SAC) [16], Proximal Policy Optimization (PPO) [17], and Deep Q Networks (DQN)-to enable the prosthetic hand to autonomously grasp objects. To achieve the objective, the impact of each algorithm on the effectiveness of the gripping action will be evaluated [18,19] and an optimal model for a variety of objects will be tested, specifically investigating how the physical properties of the objects may significantly influence the capability of the prosthetic hand to grip them effectively.…”

Section: Introductionmentioning

confidence: 99%

On Automated Object Grasping for Intelligent Prosthetic Hands Using Machine Learning

Odeyemi,

Ogbeyemi,

Zhang

et al. 2023

Preprint

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This paper explores the application of machine learning techniques for automated object grasping. Current electronic prosthetics often require extensive training for users to gain fine motor control over the prosthetic fingers, hindering their usability and acceptance. To address this challenge, this paper proposes an automated method that leverages computer vision-based techniques and machine learning algorithms. In this study, three reinforcement learning algorithms, namely Soft Actor-Critic (SAC), Deep Q-Network (DQN), and Proximal Policy Optimization (PPO), are employed to train agents for automated grasping tasks. The results indicate that the SAC algorithm achieves the highest success rate of 99% among the three algorithms at just under 200,000 timesteps. This research also shows that object’s physical characteristics can affect the agent’s ability to learn an optimal policy. Moreover, the findings highlight the potential of the SAC algorithm in developing intelligent prosthetic hands with automatic object gripping capabilities.

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A New Approach to Polyp Detection by Pre-Processing of Images and Enhanced Faster R-CNN

Cited by 33 publications

References 25 publications

Colorectal Polyp Detection and Comparative Evaluation Based on Deep Learning Approaches

Colorectal Polyp Detection and Comparative Evaluation Based on Deep Learning Approaches

Identifying threat objects using faster region-based convolutional neural networks (faster R-CNN)

On Automated Object Grasping for Intelligent Prosthetic Hands Using Machine Learning

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