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Advancing Brain Tumor Diagnosis: A Hybrid Approach Using Edge Detection and Deep Learning 27th

Advancing Brain Tumor Diagnosis: A Hybrid Approach Using Edg...

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27th International conference on pattern recognition, ICPR 2024

作者： Vu, Ha Anh Vajda, Szilárd Department of Computer Science Central Washington University EllensburgWA98926 United States

ISBN: (纸本)9783031782008

Brain tumor classification from MRI scans demands precise image analysis, a challenge compounded by the variable morphology and location of tumors. Addressing this, our study presents an innovative approach that combines edge detection with a hierarchical deep learning framework to classify brain tumors accurately. This method enhances edge clarity, facilitating the deep learning model’s ability to distinguish between meningioma, glioma, and pituitary tumors. By deploying a two-stage model, initially segregating a meta-tumor class and pituitary and subsequently refining the meta-tumor class into glioma and meningioma with a binary classifier, we capitalize on the strengths of both traditional image processing and advanced neural networks. The already proven ResNet50 architecture, our model’s backbone, benefits from transfer learning, enabling efficient feature extraction from the edge image tailored to brain tumor recognition. Our results, evidenced by an over 96% overall accuracy rate obtained on a large benchmark brain tumor dataset, underscore the potential of integrating edge detection processing with deep learning. This integrative multi-level strategy promises to streamline the diagnostic process, offering a reliable, fast, and cost-effective solution that could reduce the need for expensive human specialist intervention. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Magnetic resonance imaging

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Research on image Classification Based on Neural Network VGG16 3

Research on Image Classification Based on Neural Network VGG...

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3rd International conference on Computer Vision and pattern analysis, ICCPA 2023

作者： Yuan, Fei Jiang, Xianzhong Du, Weisong Yang, Weilin School of Intelligent Equipment and Automotive Engineering Wuxi Vocational Institute of Commerce Wuxi China Discipline Enforcement Review Division Wuxi Vocational Institute of Commerce Wuxi China Wuxi Pioneer Intelligent Equipment Co. Ltd Wuxi China School of Internet of Things Engineering Jiangnan University Wuxi China

ISBN: (纸本)9781510667563

Based on PyTorch framework, this paper studies the network structure of classical convolutional neural network VGG16 and the process of input image changing in the network. Using VGG16 to train the small dataset CIFAR10 in various ways, it is found that using the Kaiming normal distribution to initialize the weight for training is far less effective than transfer learning (using the training weight of VGG16 on the imageNet dataset as the initial value of the weight). The input image of VGG16 can be of any size, but after the image of CIFAR10 is resized to 224×224, the accuracy rate on the test set is improved by 6 percentage points. © 2023 SPIE.

关键词： image classification

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Study on the classification method of gastrointestinal endoscope image based on CBAM and AlexNet 3

Study on the classification method of gastrointestinal endos...

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3rd International conference on Computer Vision and pattern analysis, ICCPA 2023

作者： Tong, Huifeng Zhou, Hua College of Big Data and Intelligent Engineering Southwest Forestry University Kunming650224 China

ISBN: (纸本)9781510667563

Gastrointestinal diseases are one of the important causes of gastrointestinal tumors, and also one of the serious public health problems that human beings are currently facing. Gastrointestinal inflammation and polyp lesions are the main ways to cause gastrointestinal cancer. Therefore, timely detection of gastrointestinal polyps and inflammation and other lesions through gastrointestinal endoscopy can effectively prevent corresponding cancer. In recent years, deep learning technology has played a huge role in computer-aided gastrointestinal endoscopic diagnosis, especially the convolutional neural network can recognize gastrointestinal lesions. This paper uses the public gastrointestinal endoscopy data set Kvasir (including 7 categories, 1000 images for each category), proposed and designed CBAM-AlexNet deep learning model, in which CBAM is the attention mechanism part, including channel attention and spatial attention, AlexNet is a deep learning network. The original features are input into the channel attention mechanism in CBAM to extract channel features, and then the output channel features are input into the spatial attention mechanism to extract spatial features, and finally the output spatial features are input into the AlexNet network for feature extraction and gastrointestinal disease data. The average accuracy rate, average accuracy rate, average recall rate and average F1Score of all categories of CBAM-AlexNet model were 91.36%, 91.34%, 91.39% and 0.913, respectively, and achieved good classification effect in seven categories of gastrointestinal endoscopy data, showing the superiority of the model. CBAM-AlexNet model has high specificity and sensitivity for the recognition of gastrointestinal polyps, which can assist doctors in the clinical diagnosis of gastrointestinal polyps quickly and reduce the probability of misdiagnosis, Some recognition errors caused by quality issues such as overexposure or underexposure of images will be improved in future wo

关键词： Endoscopy

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Computer Vision and pattern recognition Technology on Account of Deep Neural Network 4th

Computer Vision and Pattern Recognition Technology on Accoun...

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4th International conference on Machine Learning, image Processing, Network Security and Data Sciences (MIND)

作者： Ren, Yiming Han, Jiake Chen, Chongyu Xu, Yang Bao, Tingxuan Shenzhen MSU BIT Univ Shenzhen 518172 Guangdong Peoples R China Shanghai Univ Shanghai 201900 Peoples R China Tianjin Univ Sci & Technol 1038 Dagu South Rd Tianjin Hexi Tianjin 300222 Peoples R China

ISBN: (纸本)9783031243660;9783031243677

With the rapid development of computer, computer vision technology is also making rapid progress. In this paper, deep neural network algorithm is used to improve the technology of computer vision, improve the visual effect, and at the same time, innovative algorithm structure, improve the identification of pattern recognition. Computer vision and pattern recognition is a very cutting edge technology that has given people very advanced tools for vision and recognition. At present, the technology can control more accurate image resolution and improve the ability of pattern recognition. This article mainly explains the process of using deep neural network algorithm to improve computer vision and pattern recognition from the internal mechanism, and reveals the working principle and internal mechanism of computer vision and pattern recognition technology application. Data analysis proves that the pattern recognition application established by deep neural network algorithm performs very well in the field of vision and pattern recognition.

关键词： Deep neural network algorithm Computer vision pattern recognition Technical application

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Advanced allowance planning of CFRP composites exploiting the pattern of chopped carbon fibre reinforcement clusters 3

Advanced allowance planning of CFRP composites exploiting th...

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3rd CIRP conference on Composite Material Parts Manufacturing, CCMPM 2024

作者： Geier, Norbert Magyar, Gergely Budapest University of Technology and Economics Faculty of Mechanical Engineering Department of Manufacturing Science and Engineering Muegyetem rakpart 3 Budapest1111 Hungary

Conventional allowance planning of carbon fibre-reinforced polymer composite plates that must be mechanically machined is based on mainly the analysis of the precision of composite manufacturing technologies. This approach neglects the impact of randomly oriented and positioned chopped fibre reinforcement clusters leading to unpredictable fibre cutting angles and inconsistent quality during machining. To address this issue, we developed an innovative allowance planning method for polymer composites reinforced with chopped fibres. Our approach optimizes the size of the non-uniform allowance to minimize machining-induced burrs on the machined edges by detecting fibre reinforcement clusters on the composite surface through digital image processing and employing a convolution-based optimization of geometric feature patterns. Validation through drilling experiments demonstrated that our method improved the average burr factor by 50% compared to a conventional allowance planning technique. Although the proposed method is recommended to be improved to manage the effects of three-dimensional fibre clusters on burr occurrence, it encourages a novel direction in allowance planning of composites having non-defined directional reinforcements. © 2025 The Author(s).

关键词： Carbon fiber reinforced plastics

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A Coarse-to-Fine Ensembling Framework for Head and Neck Tumor and Lymph Segmentation in CT and PET images 1

引用

3rd 3D Head and Neck Tumor Segmentation in PET/CT Challenge, HECKTOR 2022, held in Conjunction with the 25th International conference on Medical image Computing and Computer Assisted Intervention, MICCAI 2022

作者： Sun, Xiao An, Chengyang Wang, Lisheng Department of Automation Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9783031274206

ISBN: (纸本)9783031274190

Head and neck (H &N) cancer is one of the most prevalent cancers [1]. In its treatment and prognosis analysis, tumors and metastatic lymph nodes may play an important role but their manual segmentations are time-consuming and laborious. In this paper, we propose a coarse-to-fine ensembling framework to segment the H &N tumor and metastatic lymph nodes automatically from Positron Emission Tomography (PET) and Computed Tomography (CT) images. The framework consists of three steps. The first step is to locate the head region in CT images. The second step is a coarse segmentation, to locate the tumor and lymph region of interest (ROI) from the head region. The last step is a fine segmentation, to get the final precise predictions of tumors and metastatic lymph nodes, where we proposed a ensembling refinement model. This framework is evaluated quantitatively with aggregated Dice Similarity Coefficient (DSC) of 0.77782 in the task 1 of the HECKTOR 2022 challenge[2, 3] as team SJTU426. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Tumors

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Self-supervised image Classification Using Convolutional Neural Network

Self-supervised Image Classification Using Convolutional Neu...

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Power Electronics, Computer Applications (ICPECA), IEEE International conference on

作者： Shiyun Kong Shanghai United International School Shanghai China

image classification has been a trendy research topic in the field of pattern recognition and computer vision, which extracts different features of images and predict the category of images. Thanks to the development of deep learning, powerful convolutional neural networks can be used in the field of image recognition. However, existing deep learning-based image recognition research mostly follows the framework of supervised learning, and model learning relies on a large number of accurate labels. Providing a large amount of label data will undoubtedly require laborious human effort and expensive costs. Therefore, image recognition based on unsupervised learning (without using any image category labels to achieve classification) has become a spotlight for research. In this paper, explorations on the image classification by self-supervised framework SimCLR on image classification successfully clusters a large number of images into an optimum amount categories. Qualitative results have show SimCLR is particularly effective in recognizing the colors of images; both qualitative and quantitative results shows SimCLR is great at identifying simple contours. However, when the colors are similar, and contour lines are complex, SimCLR does not obtain satisficing results. The accuracy on classifying Mnist dataset is 32%.

关键词： image recognition image color analysis Supervised learning Feature extraction Power electronics pattern recognition Convolutional neural networks

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Data Mining of Garment pattern Based on Decision Tree Algorithm 3rd

Data Mining of Garment Pattern Based on Decision Tree Algori...

引用

3rd EAI International conference on Application of Big Data, Blockchain, and Internet of Things for Education Informatization, BigIoT-EDU 2023

作者： Li, Jing Li, Chen Shandong Vocational College of Light Industry Shandong Zibo255300 China

ISBN: (纸本)9783031631320

With the development of economy, society and culture, the traditional tailor-made clothing has been unable to meet people’s needs for personalization, fit and online customization of clothing. The clothing industry has gradually changed from the original large-scale and standardized production to flexible and personalized customization. Data mining is the process of extracting useful information from data. Data mining is a process of discovering patterns in a large amount of data by applying statistical and computational techniques to data. Decision tree algorithm is such a technology that helps to find patterns in a large amount of data. It uses decision tree to find the best method to classify objects according to their attributes or characteristics. In recent years, the application of advanced technologies, such as computer graphics, digital image processing and artificial intelligence, has brought new opportunities to the garment industry, promoted the mode update of the garment industry, and also promoted the digitalization and intelligence of garment design. Garment digital version is the key problem and research hotspot of garment digital technology. In this paper, the research and analysis of data mining of garment pattern based on decision tree algorithm. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Decision trees

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Is CNN Really Looking at Your Face? 6th

Is CNN Really Looking at Your Face?

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6th Asian conference on pattern recognition (ACPR)

作者： Kawai, Hiroya Kozu, Takashi Ito, Koichi Chen, Hwann-Tzong Aoki, Takafumi Tohoku Univ Grad Sch Informat Sci 6-6-05 Aramaki Aza Aoba Sendai Miyagi 9808579 Japan Natl Tsing Hua Univ Dept Comp Sci 101Sect 2Kuang Fu Rd Hsinchu 300044 Taiwan

ISBN: (纸本)9783031023750;9783031023743

Face recognition has dramatically improved its performance with the advent of deep learning, especially Convolutional Neural Networks (CNNs), while they have raised a new issue: the difficulty in interpreting the results. The question is what CNN looks at in a face image to identify a person. To answer this question, this paper presents a simple and novel analysis of deep face recognition based on facial parts. We evaluate the recognition accuracy of face images with specific regions masked using face segmentation labels. Our analysis clarifies what CNNs really need in face images for face recognition. The paper concludes with an application of face recognition models to general visualization methods and the problems contained in some classical face image datasets.

关键词： Face recognition Face parsing CNN Biometrics

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Test-Time Augmentation for Document image Binarization 11th

Test-Time Augmentation for Document Image Binarization

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11th iberian conference on pattern recognition and image analysis, IbPRIA 2023

作者： Rosello, Adrian Castellanos, Francisco J. Martinez-Esteso, Juan P. Gallego, Antonio Javier Calvo-Zaragoza, Jorge Department of Software and Computing Systems University of Alicante Alicante Spain

ISBN: (纸本)9783031366154

Document binarization is a well-known process addressed in the document image analysis literature, which aims to isolate the ink information from the background. Current solutions use deep learning, which requires a great amount of annotated data for training robust models. Data augmentation is known to reduce such annotation requirements, and it can be used in two ways: during training and during prediction. The latter is the so-called Test Time Augmentation (TTA), which has been successfully applied for general classification tasks. In this work, we study the application of TTA for binarization, a more complex and specific task. We focus on cases with a severe scarcity of annotated data over 5 existing binarization benchmarks. Although the results report certain improvements, these are rather limited. This implies that existing TTA strategies are not sufficient for binarization, which points to interesting lines of future work to further boost the performance. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Convolutional neural networks

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