A modified faster R-CNN method to improve the performance of the pulmonary nodule detection

Fan, Weikang; Jiang, Huiqin; Ma, Ling; Gao, Jianbo; Yang, Haojin

doi:10.1117/12.2502893

Cited by 3 publications

(2 citation statements)

References 7 publications

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“…Automatic detection of breast and pulmonary lesions using neural networks has been among the most heavily examined. Such tools will support radiologists with a “second opinion,” replace and outperform the currently used CAD, and reduce the time needed scanning millions of screening mammograms and chest x-rays 45,56–59 Table 2. summarizes the most important recent articles that have used object detection algorithms in medical imaging.…”

Section: Uses Of Ai In Medical Images Analysismentioning

confidence: 99%

Artificial Intelligence in Diagnostic Radiology: Where Do We Stand, Challenges, and Opportunities

Moawad

Fuentes

Elbanan

et al. 2022

J Comput Assist Tomogr

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Artificial intelligence (AI) is the most revolutionizing development in the health care industry in the current decade, with diagnostic imaging having the greatest share in such development. Machine learning and deep learning (DL) are subclasses of AI that show breakthrough performance in image analysis. They have become the state of the art in the field of image classification and recognition. Machine learning deals with the extraction of the important characteristic features from images, whereas DL uses neural networks to solve such problems with better performance. In this review, we discuss the current applications of machine learning and DL in the field of diagnostic radiology.Deep learning applications can be divided into medical imaging analysis and applications beyond analysis. In the field of medical imaging analysis, deep convolutional neural networks are used for image classification, lesion detection, and segmentation. Also used are recurrent neural networks when extracting information from electronic medical records and to augment the use of convolutional neural networks in the field of image classification. Generative adversarial networks have been explicitly used in generating high-resolution computed tomography and magnetic resonance images and to map computed tomography images from the corresponding magnetic resonance imaging. Beyond image analysis, DL can be used for quality control, workflow organization, and reporting.In this article, we review the most current AI models used in medical imaging research, providing a brief explanation of the various models described in the literature within the past 5 years. Emphasis is placed on the various DL models, as they are the most state-of-art in imaging analysis.

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Section: Uses Of Ai In Medical Images Analysismentioning

confidence: 99%

Artificial Intelligence in Diagnostic Radiology: Where Do We Stand, Challenges, and Opportunities

Moawad

Fuentes

Elbanan

et al. 2022

J Comput Assist Tomogr

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show abstract

“…This is also an advantage over manually hard-coded anchor boxes (e.g. [23], [24], [33]), or K-means-based anchor box clustering (e.g. [8]) approaches.…”

Section: B Adaptive Anchor Box Sizesmentioning

confidence: 99%

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

et al. 2021

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Potato Bud Detection with Improved Faster R-CNN

Xi¹,

Hou²,

Lou³

2020

Transactions of the ASABE

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Highlights A series of strategies including feature concatenation, tweaks of ResNet50, and modification of the default anchors with the chaos optimization-based k -means algorithm were proposed to improve the detection performance of the original Faster R-CNN. The improved Faster R-CNN achieved an average precision of 97.71%, which is 5.98% higher than that of the original Faster R-CNN and 14.38% higher than that of YOLOv2. The improved Faster R-CNN greatly boosted the detection performance for potato buds without incurring any noticeable additional computational overhead. Abstract. This article proposes an improved Faster R-CNN model to achieve better detection performance for potato buds, with the goal of preparing for the automated cutting of seed potatoes. Detection results of Faster R-CNNs with eight pretrained networks were compared, and ResNet50 was adopted as the backbone network in Faster R-CNN. On this basis, three model strategies, including feature concatenation, tweaks of ResNet50, and modification of the default anchors with the chaos optimization-based k-means algorithm, were proposed to improve the detection performance for potato buds. Experimental results on the test set demonstrated that the improved Faster R-CNN achieved an average precision (AP) of 97.71%, which is 5.98% higher than that of the original Faster R-CNN and 14.38% higher than that of YOLOv2. In addition, the average running time per image with the improved Faster R-CNN was 0.166 s, the same as that of the original Faster R-CNN. In other words, the improved Faster R-CNN greatly boosted the detection performance for potato buds without incurring any noticeable additional computational overhead, thus satisfying the requirements for real-time processing. Consequently, the improved Faster R-CNN can provide a solid foundation for the automated cutting of seed potatoes. Keywords: Chaos optimization-based k-means, Default anchors, Faster R-CNN, Feature concatenation, Potato bud detection, Tweaks of ResNet50.

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A modified faster R-CNN method to improve the performance of the pulmonary nodule detection

Cited by 3 publications

References 7 publications

Artificial Intelligence in Diagnostic Radiology: Where Do We Stand, Challenges, and Opportunities

Artificial Intelligence in Diagnostic Radiology: Where Do We Stand, Challenges, and Opportunities

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

Potato Bud Detection with Improved Faster R-CNN

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