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Existence and Non-Existence of Solutions to Some Degenerate Coercivity Quasilinear Elliptic Equations with Measure data

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Journal of Mathematical Research with applications 2022年第2期42卷 173-188页

作者： Maoji RI Xiangrui LI Qiaoyu TIAN Shuibo HUANG School of Mathematics and Computer Science Northwest Minzu University Key Laboratory of Streaming Data Computing Technologies and Application Northwest Minzu University

In this article,we study the existence and non-existence of weak solutions to the following quasilinear elliptic problem with principal part having degenerate coercivity and nonlinear term involving gradie,■where??RN... 详细信息

关键词： elliptic equation degenerate coercivity measures data existence non-existence

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Automatic Segmentation of Metastasis Lesions in SPECT Bone Scan Images

Automatic Segmentation of Metastasis Lesions in SPECT Bone S...

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2020 International Conference on Intelligent computing, Automation and Systems, ICICAS 2020

作者： Gao, Ruiting Lin, Qiang Luo, Mingyang Man, Zhengxing Cao, Yongchun Wang, Haijun Key Laboratory of China's Ethnic Languages and Information Technology of Ministry of Education Key Laboratory of Streaming Data Computing Technologies and Application Northwest Minzu University Lanzhou China Key Laboratory of Streaming Data Computing Technologies and Application Northwest Minzu University Lanzhou China Gansu Provincial People's Hospital Department of Nuclear Medicine Lanzhou China

ISBN: (纸本)9781728191461

SPECT bone imaging is among the main nuclear medicine functional imaging modalities, which has the potential of early diagnosis of various serious diseases such as cancers. However, the low resolution and contrast of SPECT bone images bring huge challenges to performance on segmenting hotspots or lesions in SPECT bone scan images. In this work, three different algorithms including K-means clustering method, region growth method and C-V model are introduced to segment lesions in SPECT bone scan images by fine-tuning parameters, focusing on the bone metastatic lesions area in SPECT imaging. Specifically, each of the original 256×1024 whole body images is cropped into a 256×256 thoracic region including ribs and spine, followed by the data normalization. Then, different parameters are experimentally determined for the above three algorithms, leading to three different segmentation algorithms. Last, experimental evaluation conducted on a group of real-world samples of SPECT bone scan images reveals that our methods are workable for segmenting lesions with SPECT imaging, obtaining a value of 0.7307, 0.7768 and 0.8076 for Tanimoto similarity coefficient metric by the three methods, respectively. Particularly, the C-V model based segmentation method is able to provide more assistive information for oncologists on diagnose of tumors and other related diseases. © 2020 IEEE.

关键词： Image segmentation

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CNN-Based Automated Classification of SPECT Bone Scan Images

CNN-Based Automated Classification of SPECT Bone Scan Images

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2022 International Conference on Neural Networks, Information, and Communication Engineering, NNICE 2022

作者： Man, Zhengxing Lin, Qiang Cao, Yongchun School of Mathematics and Computer Science Northwest Minzu University Gansu Lanzhou730124 China Key Laboratory of Streaming Data Computing Technologies and Application Northwest Minzu University Gansu Lanzhou730030 China

ISBN: (纸本)9781510655171

Functional medicine imaging has been successfully applied to capture functional changes in pathological tissues of the body in recent years. SPECT nuclear medicine functional imaging has the potential to acquire information about areas of concern (e.g., lesions and organs) in a non-invasive manner, enabling semi-automated or automated decision-making for the purposes of disease diagnosis, treatment, evaluation, and prediction. To reliably identify that whether or not at least one hotspot or lesion presents in a whole-body SPECT image, in this work, we develop a group of CNN-based classifiers. Specifically, we first propose a preprocessing method that transforms each original SPECT file into the required form by deep learning model, including normalization, 3-channel construction, rotation and scaling, size standardization, and size adapting. Second, six different classifiers are constructed by fine-tuning parameters of the standard VGG-16 model. Last, a group of real-world SPECT whole-body bone scan files were utilized to evaluate the developed classifiers. Experiment results shows that our classifiers are workable for the 2-class classification of SPECT images, achieving a best value of 0.7641, 0.6678, 1.000, and 0.6574 for defined evaluation metrics Acc, Pre, Rec, and AUC, respectively. © 2022 SPIE.

关键词： Image classification

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Classification of Thoracic Bone Scintigraphic Images Using ResNet with Attention Modules 2

Classification of Thoracic Bone Scintigraphic Images Using R...

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2nd International Conference on Signal Image Processing and Communication, ICSIPC 2022

作者： Cao, Xu Cao, Yongchun Lin, Qiang Man, Zhengxing Wang, Yubo Key Laboratory of China’s Ethnic Languages and Information Technology of Ministry of Education Northwest Minzu University China School of Mathematics and Computer Science Northwest Minzu University China Key Laboratory of Streaming Data Computing Technologies and Application Northwest Minzu University China

ISBN: (纸本)9781510657694

Whole-body bone scan is an effective tool for diagnosing bone metastasis from malignant tumors, including lung cancer. However, due to the low spatial resolution of Scintigraphic images, manual analysis of the images is very difficult. In order to accurately diagnose bone metastases, a self-defined convolutional neural network is studied and constructed in this paper for detecting lung cancer-Caused skeletal metastasis in bone scan images Firstly, since thoracic regions such as ribs and spine are the most frequent areas of bone metastases, we preprocessed the collected SPECT whole-body bone imaging data to extract the thoracic regions. Then based on the feature that ResNet can alleviate the vanishing gradient problem caused by increasing depth in deep neural networks and the feature that attention can focus on important information to improve network expression. A deep SPECT image classification network Att-ResNet24 with residual module and hybrid attention mechanism is developed for classification of these images, including bone metastases and non-bone metastases. Experiments on a set of real SPETC whole-body bone images show that self-defined classification networks can be effectively used for the detection of bone metastases, obtaining the best scores 0.737, 0.744, 0.736, and 0.735 for accuracy, precision, recall, and F-1 score, respectively. © 2022 SPIE.

关键词： Image classification

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Multi-disease classification of whole-body scintigraphy images based on deep learning 7

Multi-disease classification of whole-body scintigraphy imag...

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7th International Conference on Intelligent computing and Signal Processing, ICSP 2022

作者： Liu, Liangxia Cao, Yongchun Lin, Qiang Man, Zhengxing Wang, Weiqiong Key Laboratory of China's Ethnic Languages and Information Technology Ministry of Education Northwest Minzu University China School of Mathematics and Computer Science Northwest Minzu University China Key Laboratory of Streaming Data Computing Technologies and Application Northwest Minzu University China

ISBN: (纸本)9781665478571

Bone scan images has important clinical application value for the early diagnosis and treatment of skeletal lesions in the whole-body. In order to realize classification of multiple diseases (i.e., normal, bone metastases, arthritis, and thyroid cancer) in whole-body bone scintigraphy images, In this paper, the multi-disease classification method based on convolutional neural network was studied on bone scan images, and a self-defined classification model was proposed, which was named SPNT9. The model consists of nine convolutional layers, five pooling layers, two fully connected layers and one softmax layer. Finally, the model was validated with real SPECT images collected. By comparing with VGGNet, ResNet and DenseNet models, The SPNT9 model obtained the best classification performance in the test samples, with Accuracy, Precision, Recall and F-1 of 0.8814, 0.7806, 0.7900 and 0.7899, respectively. The results show that the proposed model can be used to accurately classify multiple diseases in SPECT images. © 2022 IEEE.

关键词： Convolutional neural networks

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On Star Coloring of Mycielski's Graphs for Special Graphs

On Star Coloring of Mycielski's Graphs for Special Graphs

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Computer Science and Management Technology (ICCSMT), International Conference on

作者： Cai Jin Shuangliang Tian Zhuomo An School of Mathematics and Computer Science Northwest for Minzu University Key Laboratory of Streaming Data Computing Technologies and Applications Northwest Minzu University Lanzhou China

ISBN: (纸本)9781728186696

Let G be a simple graph with vertex set V (G) and edge set E (G). A vertex coloring of G is called a star coloring of G if any path of 4 order is bicolored. The minimum number of colors required for a star coloring of G is denoted by χ s (G). Let G is a simple graph with vertex set V (G) = V and edge set E (G) = E, if the vertex set is V ∪ V' ∪ {w} where V' = {X' : x ∈ V} and the edge set is EU{xy' : xy∈V} U {y'w : y'∈V'}, then G is a Mycielski's graph. This paper mainly studies star coloring of Mycielski's graph for some special graphs, the specific results are as follows: if n = 2,3 , then χ s (M (P n )) = 4, otherwise, χ s (M (P n )) = 6 ; if n = 5, then χ s (M(C n )) = 8, otherwise, χ s (M(C n )) = 6 ; χ s (M(S n )) = 4 ; χ s (M (K n )) = n + 2.

关键词： Computer science Color

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The acyclic coloring of the Corona product and Edge corona product of special graphs

The acyclic coloring of the Corona product and Edge corona p...

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Computer Science and Management Technology (ICCSMT), International Conference on

作者： Qiqi Lu Shuangliang Tian Lizhen Peng School of Mathematics and Computer Science Northwest for Minzu University Key Laboratory of Streaming Data Computing Technologies and Applications Northwest Minzu University Lanzhou China

An acyclic coloring of a graph is a proper vertex coloring such that there are no bichromatic cycles. The acyclic chromatic number of G, denoted a(G), is the minimum number of colors required for acyclic coloring of a graph G. We study the acyclic coloring of the corona product, edge corona product of paths or circles and any simple graph, and the corresponding acyclic chromatic number are determined.

关键词： Computer science Color Corona

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Adjacent vertex distinguishing incidence coloring of semi-strong product of paths

Adjacent vertex distinguishing incidence coloring of semi-st...

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Computer Science and Management Technology (ICCSMT), International Conference on

作者： Lizhen Peng Shuangliang Tian Qiqi Lu School of Mathematics and Computer Science Northwest for Minzu University Key Laboratory of Streaming Data Computing Technologies and Applications Northwest Minzu University Lanzhou China

ISBN: (纸本)9781728186696

Adjacent vertex distinguishing incidence coloring of graph G is an incidence coloring that satisfies that adjacent vertices have different color sets, and the minimum color number is called the adjacent vertex distinguishing incidence coloring number of graph G, which is denoted as χ ai (G). In this paper, we study the adjacent vertex distinguishing incidence coloring of semi-strong products and strong products of paths of different orders, and obtain the exact values of the corresponding coloring numbers.

关键词： Computer science Color

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X-ray Classification of Tuberculosis Based on Convolutional Networks

X-ray Classification of Tuberculosis Based on Convolutional ...

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2021 IEEE International Conference on Artificial Intelligence and Industrial Design, AIID 2021

作者： Cao, Kai Zhang, Jingyi Huang, Mengge Deng, Tao Key Laboratory of China's Ethnic Languages and Information Technology of Ministry of Education Northwest Minzu University Gansu Lanzhou730000 China Key Laboratory of Streaming Data Computing Technologies and Application School of Mathematics and Computer Science Northwest Minzu University Gansu Lanzhou730000 China

ISBN: (纸本)9780738110837

Tuberculosis is a chronic infectious disease caused by Mycobacterium tuberculosis, which can invade many organs, and pulmonary tuberculosis is the most common infection. It is the key to treat tuberculosis to detect and diagnose the disease in the early stage. The existing computer-aided detection system has made preliminary progress in the diagnosis of pulmonary tuberculosis based on chest X-ray, but there is still a lack of further research on the classification of image signs of tuberculosis. In recent years, with the in-depth research and development in the field of deep learning, convolutional networks have emerged. Convolutional networks have achieved the best current results in image recognition, image classification, image segmentation, and other fields. Therefore, this paper applies the convolutional network to tuberculosis CT images and uses different convolutional network models to study the classification of tuberculosis CT images. Experiments show that the DenseNet121 model has higher performance than VGGNet16, VGGNet19, and ResNet152 models. As a result of classification, the accuracy rate is over 90%. © 2021 IEEE.

关键词： Image classification

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Classifying SPECT Bone Metastasis Images in Grayscale Format with VGGNets 3

Classifying SPECT Bone Metastasis Images in Grayscale Format...

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3rd IEEE International Conference on Communications, Information System and Computer Engineering, CISCE 2021

作者： Zhao, Shaofang Chen, Xiaoyan Zheng, Ziwen Lin, Qang School of Mathematics and Computer Sciece Northwest Minzu University Gansu Lanzhou730030 China Key Laboratory of Chinese National Language and Information Technology Ministry of Education Northwest Minzu University Gansu Lanzhou730030 China Key Laboratory of Streaming Data Computing and Applications Northwest Minzu University Gansu Lanzhou730030 China

ISBN: (纸本)9780738112152

For the purpose of accurate diagnosis of bone metastasis diseases with nuclear medicine SPECT imaging technology in this work, we propose to study and construct CNN based classification model for identifying bone metastases with real-world SPECT imaging data. First, the geometric transformation including image mirroring, rotation, and translation is used to augment dataset of SPECT bone scans. Second, the standard VGG model is adopted to develop classifier of SPECT images. Lastly, a set of real-world data of whole-body SPECT bone scans is used to evaluate the built model, obtaining a best value of 0.996, 0.995, 0.997 and 0.995 for accuracy Acc, precision Pre, recall Rec and F-1 score are, respectively. © 2021 IEEE.

关键词： Image classification

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