A novel YOLOv3-arch model for identifying cholelithiasis and classifying gallstones on CT images

Pang, Shanchen; Ding, Tonghai; Qiao, Sibo; Fan, Meng; Wang, Shuo; Li, Pibao; Wang, Xun

doi:10.1371/journal.pone.0217647

Cited by 67 publications

(25 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Different research studies and experiments on med-ical imaging were performed by the researchers using the ML approaches. Shenzhen, Pang et al (19) identified the cholelithiasis and classified the gallstone from CT scan images using YOLO model. Study results showed that consequently, the highest accuracy was achieved as 92.7% in identifying the granular gallstone in their study.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

A Comparative Analysis of Data Augmentation Approaches for Magnetic Resonance Imaging (MRI) Scan Images of Brain Tumor

2020

View full text Add to dashboard Cite

Introduction: Machine Learning (ML) is a rapidly growing subfield of Artificial Intelligence (AI). It is used for different purposes in our daily life such as face recognition, speech recognition, text translation in different languages, weather prediction, and business prediction. In parallel, ML also plays an important role in the medical domain such as in medical imaging. ML has various algorithms that need to be trained with large volumes of data to produce a well-trained model for prediction. Aim: The aim of this study is to highlight the most suitable Data Augmentation (DA) technique(s) for medical imaging based on their results. Methods: DA refers to different approaches that are used to increase the size of datasets. In this study, eight DA approaches were used on publicly available low-grade glioma tumor datasets obtained from the Tumor Cancer Imaging Archive (TCIA) repository. The dataset included 1961 MRI brain scan images of low-grade glioma patients. You Only Look Once (YOLO) version 3 model was trained on the original dataset and the augmented datasets separately. A neural network training/testing ecosystem named as supervisely with Tesla K80 GPU was used for YOLO v3 model training on all datasets. Results: The results showed that the DA techniques rotate at 180o and rotate at 90o performed the best as data enhancement techniques for medical imaging. Conclusion: Rotation techniques are found significant to enhance the low volume of medical imaging datasets.

show abstract

Section: Discussionmentioning

confidence: 99%

“…Shanzen Pang et al (19) identified the cholelithiasis and classified the gallstone from CT scan images. They created their dataset by collecting 223846 CT scan images of 1369 patients.…”

Section: Literature Reviewmentioning

confidence: 99%

A Comparative Analysis of Data Augmentation Approaches for Magnetic Resonance Imaging (MRI) Scan Images of Brain Tumor

2020

View full text Add to dashboard Cite

show abstract

“…Deep learning has gradually become the mainstream in target detection after 2012 [ 15 ]. At present, many efficient target detection networks have been proposed and applied in the industrial field, such as yolo [ 16 – 19 ], Faster R-CNN [ 20 ], and NAS-FPN [ 21 ]. The yolo algorithm was proposed by Redmon et al After two years of development, it has grown from yolo to yolov3.…”

Section: Methodsmentioning

confidence: 99%

A Robotic grinding station based on an industrial manipulator and vision system

2021

View full text Add to dashboard Cite

Due to ever increasing precision and automation demands in robotic grinding, the automatic and robust robotic grinding workstation has become a research hot-spot. This work proposes a grinding workstation constituting of machine vision and an industrial manipulator to solve the difficulty of positioning rough metal cast objects and automatic grinding. Faced with the complex characteristics of industrial environment, such as weak contrast, light nonuniformity and scarcity, a coarse-to-fine two-step localization strategy was used for obtaining the object position. The deep neural network and template matching method were employed for determining the object position precisely in the presence of ambient light. Subsequently, edge extraction and contour fitting techniques were used to measure the position of the contour of the object and to locate the main burr on its surface after eliminating the influence of burr. The grid method was employed for detecting the main burrs, and the offline grinding trajectory of the industrial manipulator was planned with the guidance of the coordinate transformation method. The system greatly improves the automaticity through the entire process of loading, grinding and unloading. It can determine the object position and target the robotic grinding trajectory by the shape of the burr on the surface of an object. The measurements indicate that this system can work stably and efficiently, and the experimental results demonstrate the high accuracy and high efficiency of the proposed method. Meanwhile, it could well overcome the influence of the materials of grinding work pieces, scratch and rust.

show abstract

“…Initially, YOLO was a convolutional network designed to detect and frame objects in an image. Subsequently, it was used to classify these objects and, then, segment the framing areas [37][38][39]. YOLO v3 [40] is extremely fast and treats the detection of the regions of interest as a regression problem by dividing the input image into a grid of size m × m, and for each cell in that grid it determines the probability that it belongs to a class of interest.…”

Section: Using Deep Cnnmentioning

confidence: 99%

Segmentation of Vegetation and Flood from Aerial Images Based on Decision Fusion of Neural Networks

Ichim

Popescu

2020

Remote Sensing

View full text Add to dashboard Cite

The detection and evaluation of flood damage in rural zones are of great importance for farmers, local authorities, and insurance companies. To this end, the paper proposes an efficient system based on five neural networks to assess the degree of flooding and the remaining vegetation. After a previous analysis the following neural networks were selected as primary classifiers: you only look once network (YOLO), generative adversarial network (GAN), AlexNet, LeNet, and residual network (ResNet). Their outputs were connected in a decision fusion scheme, as a new convolutional layer, considering two sets of components: (a) the weights, corresponding to the proven accuracy of the primary neural networks in the validation phase, and (b) the probabilities generated by the neural networks as primary classification results in the operational (testing) phase. Thus, a subjective behavior (individual interpretation of single neural networks) was transformed into a more objective behavior (interpretation based on fusion of information). The images, difficult to be segmented, were obtained from an unmanned aerial vehicle photogrammetry flight after a moderate flood in a rural region of Romania and make up our database. For segmentation and evaluation of the flooded zones and vegetation, the images were first decomposed in patches and, after classification the resulting marked patches were re-composed in segmented images. From the performance analysis point of view, better results were obtained with the proposed system than the neural networks taken separately and with respect to some works from the references.

show abstract

A novel YOLOv3-arch model for identifying cholelithiasis and classifying gallstones on CT images

Cited by 67 publications

References 12 publications

A Comparative Analysis of Data Augmentation Approaches for Magnetic Resonance Imaging (MRI) Scan Images of Brain Tumor

A Comparative Analysis of Data Augmentation Approaches for Magnetic Resonance Imaging (MRI) Scan Images of Brain Tumor

A Robotic grinding station based on an industrial manipulator and vision system

Segmentation of Vegetation and Flood from Aerial Images Based on Decision Fusion of Neural Networks

Contact Info

Product

Resources

About