Pulmonary Nodule Detection Based on Faster R-CNN With Adaptive Anchor Box

Nguyen, Cuu Khoa; Tran, Giang Son; Nguyen, Van Thi; Burie, Jean-Christophe; Nghiem, Thi Phuong

doi:10.1109/access.2021.3128942

Cited by 26 publications

(9 citation statements)

References 46 publications

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“…The proposal developed in this article is based on automating a manual process that takes up to 10 min to complete. As it is a process that does not need to be performed at high speed, it is proposed to use the Faster R-CNN object-detection algorithm to detect objects in real time, which has already been widely used [25][26][27][28].…”

Section: Article Data Type Modelmentioning

confidence: 99%

Disease and Defect Detection System for Raspberries Based on Convolutional Neural Networks

et al. 2021

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Raspberries are fruit of great importance for human beings. Their products are segmented by quality. However, estimating raspberry quality is a manual process carried out at the reception of the fruit processing plant, and is thus exposed to factors that could distort the measurement. The agriculture industry has increased the use of deep learning (DL) in computer vision systems. Non-destructive and computer vision equipment and methods are proposed to solve the problem of estimating the quality of raspberries in a tray. To solve the issue of estimating the quality of raspberries in a picking tray, prototype equipment is developed to determine the quality of raspberry trays using computer vision techniques and convolutional neural networks from images captured in the visible RGB spectrum. The Faster R–CNN object-detection algorithm is used, and different pretrained CNN networks are evaluated as a backbone to develop the software for the developed equipment. To avoid imbalance in the dataset, an individual object-detection model is trained and optimized for each detection class. Finally, both hardware and software are effectively integrated. A conceptual test is performed in a real industrial scenario, thus achieving an automatic evaluation of the quality of the raspberry tray, in this way eliminating the intervention of the human expert and eliminating errors involved in visual analysis. Excellent results were obtained in the conceptual test performed, reaching in some cases precision of 100%, reducing the evaluation time per raspberry tray image to 30 s on average, allowing the evaluation of a larger and representative sample of the raspberry batch arriving at the processing plant.

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Section: Article Data Type Modelmentioning

confidence: 99%

Disease and Defect Detection System for Raspberries Based on Convolutional Neural Networks

et al. 2021

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“…Challenges posed by variations in nodule size and shape are a noteworthy issue, and it is difficult to establish a universal diagnostic criterion because lung nodules are characterized by both size and irregular shape. Based on this, Gu Junhua et al [39] and Nguyen et al [40] proposed different ideas.…”

Section: Based On Changes In Nodule Size and Shapementioning

confidence: 99%

“…It is therefore difficult to strike a good balance between the two. However, some researchers have broken this limitation, for Deep Learning-based Lung Nodule Detection: A Review example, Nguyen et al [40] achieved a low false positive rate of 1.72 FPs/s with a sensitivity of 95.64%, which may be due to their proposed Mean-shift technique.…”

Section: Comparisonmentioning

confidence: 99%

Deep learning-based lung nodule detection: a review

Zhou

2024

EAI Endorsed Trans e-Learn

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CT scan acquisition is fast and cost-effective and has become the main lung imaging tool. However, the increase in large numbers of CT scans has placed a heavy burden on radiologists; therefore, automated lung nodule detection techniques are needed to reduce the workload of radiologists and computer-aided detection systems are proposed for further accurate diagnosis of the condition. This review provides a comprehensive overview of recent automated lung nodule detection techniques and challenges, etc., as well as a detailed overview and discussion of current research gaps, future developments, and research trends. Relevant articles published in databases such as IEEE Xplore, Science Direct, PubMed, and Web of Science cover research algorithms published from 2014 to 2023, mainly discussing deep learning-based techniques. The schemes presented in these articles, the databases used, the experimental results, and the performance of the algorithms are compared and discussed. This work aims to introduce researchers and readers to the latest techniques and their advances in the detection of lung nodules in the last decade, which will help researchers and radiologists to further understand the latest techniques in this field.

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“…In the research conducted by Nguyen et al [23], the Faster R-CNN framework was employed to detect lung nodules in CT scans. The system that was proposed in that study was able to attain a remarkably high sensitivity of 95.64% at a rate of 1.72 false positives per scan.…”

Section: Introductionmentioning

confidence: 99%

Integration of the Faster R-CNN Algorithm for Waste Detection in an Android Application

Al Amin,

Setyadarma,

Wibisono

2023

RIA

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The surge in global population and economic activity has precipitated a significant escalation in waste generation, with projections indicating potential daily global waste levels of 11 million tons by the century's conclusion. This intensification presents a formidable challenge requiring innovative solutions, one of which is the utilization of machine learning algorithms for automated waste categorization. This study explores the integration of the Faster R-CNN algorithm within an Android application, aimed at streamlining waste management through the identification and categorization of recyclables. The proliferation of smartphone usage-specifically Android applicationsprovides an accessible platform for mass education on waste management, thereby contributing to potential waste reduction. This study deployed a visual testing approach to evaluate the application's performance across diverse waste categories, including cardboard, glass, plastic, and paper. During each testing session, images of each waste type were captured and the objects were methodically rotated by approximately 20 degrees, enhancing the robustness of the machine learning model. The implementation of the Faster R-CNN algorithm within an Android environment, as exemplified in this study, has demonstrated noteworthy potential to revolutionize waste management. An impressive accuracy rate of 98.106% was achieved in the detection and classification of various waste types. Such a technological innovation can augment the efficiency of waste categorization and recycling efforts. Thus, the study contributes to the development of a more sustainable and environmentally responsible waste management system.

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Pulmonary Nodule Detection Based on Faster R-CNN With Adaptive Anchor Box

Cited by 26 publications

References 46 publications

Disease and Defect Detection System for Raspberries Based on Convolutional Neural Networks

Disease and Defect Detection System for Raspberries Based on Convolutional Neural Networks

Deep learning-based lung nodule detection: a review

Integration of the Faster R-CNN Algorithm for Waste Detection in an Android Application

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