共查询到20条相似文献，搜索用时 0 毫秒
1.
S. Kumarganesh M. Suganthi 《International journal of imaging systems and technology》2016,26(4):237-242
Medical image processing plays an important role in brain tissue detection and segmentation. In this paper, a computer aided detection of brain tissue compression based on the estimation of the location of the brain tumor. The proposed system detects and segments the brain tissues and brain tumor using mathematical morphological operations. Further, the brain tissue with tumor is compressed using lossless compression technique and the brain tissue without tumor is compressed using lossy compression technique. The proposed method achieves 96.46% sensitivity, 99.20% specificity and 98.73% accuracy for the segmentation of white matter regions from the brain. The proposed method achieves 98.16% sensitivity, 99.36% specificity and 98.78% accuracy for the segmentation of cerebrospinal fluid (CSF) regions from the brain and also achieves 93.07% sensitivity, 98.79% specificity and 97.63% accuracy for the segmentation of grey matter regions from the brain. This paper focus the brain tissue compression based on the location of brain tumor. The grey matter of the brain is applied to lossless compression due to the presence of the tumor in grey matter of the brain. The proposed system achieves 29.23% of compression ratio for compressing the grey matter of the brain region. The white matter and CSF regions of the brain are applied to lossy compression due to the non‐presence of the tumor. The proposed system achieves 39.13% of compression ratio for compressing the white matter and also achieves 37.5% of compression ratio for compressing the CSF tissue. © 2016 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 26, 237–242, 2016 相似文献
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Eatedal Alabdulkreem Jaber S. Alzahrani Majdy M. Eltahir Abdullah Mohamed Manar Ahmed Hamza Abdelwahed Motwakel Mohamed I. Eldesouki Mohammed Rizwanullah 《计算机、材料和连续体（英文）》2022,73(2):3039-3055
Big health data collection and storing for further analysis is a challenging task because this knowledge is big and has many features. Several cloud-based IoT health providers have been described in the literature previously. Furthermore, there are a number of issues related to time consumed and overall network performance when it comes to big data information. In the existing method, less performed optimization algorithms were used for optimizing the data. In the proposed method, the Chaotic Cuckoo Optimization algorithm was used for feature selection, and Convolutional Support Vector Machine (CSVM) was used. The research presents a method for analyzing healthcare information that uses in future prediction. The major goal is to take a variety of data while improving efficiency and minimizing process time. The suggested method employs a hybrid method that is divided into two stages. In the first stage, it reduces the features by using the Chaotic Cuckoo Optimization algorithm with Levy flight, opposition-based learning, and distributor operator. In the second stage, CSVM is used which combines the benefits of convolutional neural network (CNN) and SVM. The CSVM modifies CNN’s convolution product to learn hidden deep inside data sources. For improved economic flexibility, greater protection, greater analytics with confidentiality, and lower operating cost, the suggested approach is built on fog computing. Overall results of the experiments show that the suggested method can minimize the number of features in the datasets, enhances the accuracy by 82%, and decrease the time of the process. 相似文献
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Ngangbam Herojit Singh;N. R. Gladiss Merlin;R. Thandaiah Prabu;Deepak Gupta;Meshal Alharbi; 《International journal of imaging systems and technology》2024,34(1):e22951
Brain tumors are still diagnosed and classified based on the results of histopathological examinations of biopsy samples. The existing method requires extra effort from the user, takes too long, and can lead to blunders. These limitations underline the need of employing a fully automated deep learning system for the multi-classification of brain tumors. In order to facilitate early detection, this study employs a convolutional neural network (CNN) to multi-classify brain tumors. In this research, we present three distinct CNN models for use in three separate categorization tasks. The first CNN model can correctly categorize brain tumors 99.74% of the time. The second CNN model is 96.27% accurate in differentiating between normal, glioma, meningioma, pituitary, and metastatic brain tumors. The third CNN model successfully distinguishes between Grades II, III, and IV brain tumors 99.18% of the time. The Hybrid Particle Swarm Grey Wolf Optimization (HPSGWO) technique is used to quickly and accurately determine optimal values for all of CNN models most important hyperparameters. An HPSGWO algorithm is used to fine-tune all the necessary hyperparameters for optimal classification performance. The results are compared with standard existing CNN models across a range of performance measures. The proposed models are trained using publicly available large clinical datasets. To verify their initial multi-classification of brain tumors, clinicians and radiologists might use the proposed CNN models. 相似文献
4.
Latifa Houria Noureddine Belkhamsa Assia Cherfa Yazid Cherfa 《International journal of imaging systems and technology》2023,33(2):610-621
Magnetic resonance imaging (MRI) is increasingly used in the diagnosis of Alzheimer's disease (AD) in order to identify abnormalities in the brain. Indeed, cortical atrophy, a powerful biomarker for AD, can be detected using structural MRI (sMRI), but it cannot detect impairment in the integrity of the white matter (WM) preceding cortical atrophy. The early detection of these changes is made possible by the novel MRI modality known as diffusion tensor imaging (DTI). In this study, we integrate DTI and sMRI as complementary imaging modalities for the early detection of AD in order to create an effective computer-assisted diagnosis tool. The fused Bag-of-Features (BoF) with Speeded-Up Robust Features (SURF) and modified AlexNet convolutional neural network (CNN) are utilized to extract local and deep features. This is applied to DTI scalar metrics (fractional anisotropy and diffusivity metric) and segmented gray matter images from T1-weighted MRI images. Then, the classification of local unimodal and deep multimodal features is first performed using support vector machine (SVM) classifiers. Then, the majority voting technique is adopted to predict the final decision from the ensemble SVMs. The study is directed toward the classification of AD versus mild cognitive impairment (MCI) versus cognitively normal (CN) subjects. Our proposed method achieved an accuracy of 98.42% and demonstrated the robustness of multimodality imaging fusion. 相似文献
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基于卷积神经网络模型的遥感图像分类 总被引：2，自引：0，他引：2
研究了遥感图像的分类,针对遥感图像的支持向量机(SVM)等浅层结构分类模型特征提取困难、分类精度不理想等问题,设计了一种卷积神经网络(CNN)模型,该模型包含输入层、卷积层、全连接层以及输出层,采用Soft Max分类器进行分类。选取2010年6月6日Landsat TM5富锦市遥感图像为数据源进行了分类实验,实验表明该模型采用多层卷积池化层能够有效地提取非线性、不变的地物特征,有利于图像分类和目标检测。针对所选取的影像,该模型分类精度达到94.57%,比支持向量机分类精度提高了5%,在遥感图像分类中具有更大的优势。 相似文献
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Rafid Mostafiz Mosaddik Hasan Imran Hossain Mohammad M. Rahman 《International journal of imaging systems and technology》2020,30(1):224-233
This paper presents an intelligent system for gastrointestinal polyp detection in endoscopic video. Video endoscopy is a popular diagnostic modality in assessing the gastrointestinal polyps. But the accuracy of diagnosis mostly depends on doctors' experience that is crucial to detect polyps in many cases. Computer-aided polyp detection is promising to reduce the miss detection rate of polyp and thus improve the accuracy of diagnosis results. The proposed method illustrates an automatic system based on a new color feature extraction scheme as a support for gastrointestinal polyp detection. The scheme is the combination of color empirical mode decomposition features and convolutional neural network features extracted from video frames. The features are fed into a linear support vector machine to train the classifier. Experiments on standard public databases show that the proposed scheme outperforms the previous conventional methods, gaining accuracy of 99.53%, sensitivity of 99.91%, and specificity of 99.15%. 相似文献
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Jingyu Hu Xiaojing Gu Xingsheng Gu 《International journal of imaging systems and technology》2021,31(1):364-378
Multimodal medical image data provide different structured and functional information, which helps segment brain tumor and gets a reliable and accurate diagnosis. Segmenting brain tumors in magnetic resonance imaging (MRI) is a challenging task because brain tumors can be at any location with changeable shape and size. Existing deep neural networks for brain tumor segmentation use few connections to fuse multilevel information. To make use of multilevel information from multimodal MRIs, we propose dual‐pathway DenseNets with fully lateral connections (DP‐DenseNets), a three‐dimensional (3D) fully convolutional neural network that uses dense connectivity to construct dual‐pathway architecture to multimodal brain tumor segmentation problem. Each two similar imaging modalities have a pathway, for one thing, the bottom‐up pathway with dense connectivity is developed for extracting features; another, the top‐down pathway concatenates the features of the bottom‐up pathway in all layers. Dual pathways with different loss functions and fully lateral connectivity from the bottom‐up pathway to the top‐down pathway provide an abundant combination of different levels of features. Comparing to these fusion schemes such as input‐level fusion and later‐level fusion, this architecture leverages semantics from low to high levels, which is provided by fully lateral connectivity. Our model is evaluated on the dataset from Brain Tumor Segmentation Challenge 2017 (BRATS 2017), and the experiments show that our method achieves better performance than other 3D networks. 相似文献
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Gurinderjeet Kaur Prashant Singh Rana Vinay Arora 《International journal of imaging systems and technology》2023,33(1):340-361
To propose and implement an automated machine learning (ML) based methodology to predict the overall survival of glioblastoma multiforme (GBM) patients. In the proposed methodology, we used deep learning (DL) based 3D U-shaped Convolutional Neural Network inspired encoder-decoder architecture to segment the brain tumor. Further, feature extraction was performed on these segmented and raw magnetic resonance imaging (MRI) scans using a pre-trained 2D residual neural network. The dimension-reduced principal components were integrated with clinical data and the handcrafted features of tumor subregions to compare the performance of regression-based automated ML techniques. Through the proposed methodology, we achieved the mean squared error (MSE) of 87 067.328, median squared error of 30 915.66, and a SpearmanR correlation of 0.326 for survival prediction (SP) with the validation set of Multimodal Brain Tumor Segmentation 2020 dataset. These results made the MSE far better than the existing automated techniques for the same patients. Automated SP of GBM patients is a crucial topic with its relevance in clinical use. The results proved that DL-based feature extraction using 2D pre-trained networks is better than many heavily trained 3D and 2D prediction models from scratch. The ensembled approach has produced better results than single models. The most crucial feature affecting GBM patients' survival is the patient's age, as per the feature importance plots presented in this work. The most critical MRI modality for SP of GBM patients is the T2 fluid attenuated inversion recovery, as evident from the feature importance plots. 相似文献
10.
为了解决异种材料扩散焊质量超声波检测时,从回波幅度无法判断界面是否存在微小缺陷的问题,采用支持向量机技术构建了扩散焊界面缺陷识别模型.以TiAl和40Cr扩散焊接头为研究对象,采用超声波水浸聚焦法采集扩散焊界面信号,从信号中提取4个特征值,优化样本数量和核参数后,训练扩散焊界面缺陷识别模型.扩散焊试样界面信号经模型识别后,根据C扫描图像的位置重构识别图像.结果表明,该模型有效地识别出未焊合、弱接合和微孔缺陷,3种缺陷的正确识别率分别为93%、90.5%和91.5%,识别图像直观地显示了界面的缺陷.TiAl/40Cr扩散焊界面缺陷识别模型实现了扩散焊试样界面缺陷的智能识别. 相似文献
11.
Dan Huang;Luyi Qiu;Zifeng Liu;Yi Ding;Mingsheng Cao; 《International journal of imaging systems and technology》2024,34(4):e23135
In clinical diagnosis and surgical planning, extracting brain tumors from magnetic resonance images (MRI) is very important. Nevertheless, considering the high variability and imbalance of the brain tumor datasets, the way of designing a deep neural network for accurately segmenting the brain tumor still challenges the researchers. Moreover, as the number of convolutional layers increases, the deep feature maps cannot provide fine-grained spatial information, and this feature information is useful for segmenting brain tumors from the MRI. Aiming to solve this problem, a brain tumor segmenting method of residual multilevel and multiscale framework (Res-MulFra) is proposed in this article. In the proposed framework, the multilevel is realized by stacking the proposed RMFM-based segmentation network (RMFMSegNet), which is mainly used to leverage the prior knowledge to gain a better brain tumor segmentation performance. The multiscale is implemented by the proposed RMFMSegNet, which includes both the parallel multibranch structure and the serial multibranch structure, and is mainly designed for obtaining the multiscale feature information. Moreover, from various receptive fields, a residual multiscale feature fusion module (RMFM) is also proposed to effectively combine the contextual feature information. Furthermore, in order to gain a better brain tumor segmentation performance, the channel attention module is also adopted. Through assessing the devised framework on the BraTS dataset and comparing it with other advanced methods, the effectiveness of the Res-MulFra is verified by the extensive experimental results. For the BraTS2015 testing dataset, the Dice value of the proposed method is 0.85 for the complete area, 0.72 for the core area, and 0.62 for the enhanced area. 相似文献
12.
The durability performance of reinforced concrete (RC) building structures is significantly affected by the corrosion of the steel reinforcement due to chloride penetration, thus, the chloride ion diffusion coefficient should be investigated through experiments or theoretical equations to assess the durability of an RC structure. This study aims to predict the chloride ion diffusion coefficient of concrete, a heterogeneous material. A convolutional neural network (CNN)-based regression model that learns the condition of the concrete surface through deep learning, is developed to efficiently obtain the chloride ion diffusion coefficient. For the model implementation to determine the chloride ion diffusion coefficient, concrete mixes with w/c ratios of 0.33, 0.40, 0.46, 0.50, 0.62, and 0.68, are cured for 28 days; subsequently, the surface image data of the specimens are collected. Finally, the proposed model predicts the chloride ion diffusion coefficient using the concrete surface image data and exhibits an error of approximately 1.5E−12 /s. The results suggest the applicability of proposed model to the field of facility maintenance for estimating the chloride ion diffusion coefficient of concrete using images. 相似文献
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K. Vijila Rani S. Joseph Jawhar 《International journal of imaging systems and technology》2020,30(4):899-915
Lung tumor is a complex illness caused by irregular lung cell growth. Earlier tumor detection is a key factor in effective treatment planning. When assessing the lung computed tomography, the doctor has many difficulties when determining the precise tumor boundaries. By offering the radiologist a second opinion and helping to improve the sensitivity and accuracy of tumor detection, the use of computer-aided diagnosis could be near as effective. In this research article, the proposed Lung Tumor Detection Algorithm consists of four phases: image acquisition, preprocessing, segmentation, and classification. The Advance Target Map Superpixel-based Region Segmentation Algorithm is proposed for segmentation purposes, and then the tumor region is measured using the nanoimaging theory. Using the concept of boosted deep convolutional neural network yields 97.3% precision, image recognition can be achieved. In the types of literature with the current method, which shows the study's proposed efficacy, the implementation of the proposed approach is found dramatically. 相似文献
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Parneet Kaur Yogesh Kumar Shakeel Ahmed Abdulaziz Alhumam Ruchi Singla Muhammad Fazal Ijaz 《计算机、材料和连续体（英文）》2022,71(1):35-50
Automatic License Plate Recognition (ALPR) systems are important in Intelligent Transportation Services (ITS) as they help ensure effective law enforcement and security. These systems play a significant role in border surveillance, ensuring safeguards, and handling vehicle-related crime. The most effective approach for implementing ALPR systems utilizes deep learning via a convolutional neural network (CNN). A CNN works on an input image by assigning significance to various features of the image and differentiating them from each other. CNNs are popular for license plate character recognition. However, little has been reported on the results of these systems with regard to unusual varieties of license plates or their success at night. We present an efficient ALPR system that uses a CNN for character recognition. A combination of pre-processing and morphological operations was applied to enhance input image quality, which aids system efficiency. The system has various features, such as the ability to recognize multi-line, skewed, and multi-font license plates. It also works efficiently in night mode and can be used for different vehicle types. An overall accuracy of 98.13% was achieved using the proposed CNN technique. 相似文献
15.
针对轨道电路故障诊断准确率低且高质量故障数据难以收集等问题,提出一种基于单分类支持向量机 (OC-SVM) 与深度神经网络 (DNN) 相结合的故障诊断方法。该方法使用OC-SVM模型对数据进行单分类识别,将正样本数据输入到DNN模型进行训练和预测,为负样本数据添加标签并收集。利用ZPW-2000R轨道电路信号数据进行大量实验,结果表明OC-SVM模型能精确地识别出正负样本数据,DNN模型能准确高效地诊断出15种数据类型,且准确率高达99%。与粒子群算法优化支持向量机、卷积神经网络、堆叠自编码器3种故障诊断方法相比,该组合方法的准确率更高,诊断效果更稳定。
相似文献16.
特大断面地下洞库爆破开挖工程中涉及到众多的影响因素,为了较准确地预测出爆破振动速度,引入支持向量机理论,建立最小二成支持向量机爆破振动速度预测模型(LS-SVM模型),该模型利用结构风险最小化来提高求解问题的速度和精度。采用该模型对某地下水封LPG洞库工程进行爆破振动速度预测,并与传统的萨道夫斯基回归公式模型(萨氏模型)和模糊神经网络模型(FNN模型)进行对比分析。分析结果表明:LS-SVM模型、FNN模型与萨氏模型的全局均方根相对误差RMSRE分别为4.68%、14.42%与19.33%;LS-SVM模型有14组数据满足预测模型泛化能力误差阀值(6%)的要求,而FNN模型与萨氏模型均不满足要求。因此LS-SVM模型在爆破振动速度预测中的预测性能和泛化能力均优于FNN模型及萨氏模型,可为多因素影响下类似工程爆破振动速度预测提供借鉴经验。 相似文献
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Abdullah A. Asiri Amna Iqbal Javed Ferzund Tariq Ali Muhammad Aamir Khalaf A. Alshamrani Hassan A. Alshamrani Fawaz F. Alqahtani Muhammad Irfan Ali H. D. Alshehri 《计算机、材料和连续体（英文）》2022,73(1):641-655
Abnormal growth of brain tissues is the real cause of brain tumor. Strategy for the diagnosis of brain tumor at initial stages is one of the key step for saving the life of a patient. The manual segmentation of brain tumor magnetic resonance images (MRIs) takes time and results vary significantly in low-level features. To address this issue, we have proposed a ResNet-50 feature extractor depended on multilevel deep convolutional neural network (CNN) for reliable images segmentation by considering the low-level features of MRI. In this model, we have extracted features through ResNet-50 architecture and fed these feature maps to multi-level CNN model. To handle the classification process, we have collected a total number of 2043 MRI patients of normal, benign, and malignant tumor. Three model CNN, multi-level CNN, and ResNet-50 based multi-level CNN have been used for detection and classification of brain tumors. All the model results are calculated in terms of various numerical values identified as precision (P), recall (R), accuracy (Acc) and f1-score (F1-S). The obtained average results are much better as compared to already existing methods. This modified transfer learning architecture might help the radiologists and doctors as a better significant system for tumor diagnosis. 相似文献
19.
针对3D-CNN能够较好地提取视频中时空特征但对计算量和内存要求很高的问题,本文设计了高效3D卷积块替换原来计算量大的3×3×3卷积层,进而提出了一种融合3D卷积块的密集残差网络(3D-EDRNs)用于人体行为识别.高效3D卷积块由获取视频空间特征的1×3×3卷积层和获取视频时间特征的3×1×1卷积层组合而成.将高效3... 相似文献
20.
As a common medium in our daily life, images are important for most peopleto gather information. There are also people who edit or even tamper images todeliberately deliver false information under different purposes. Thus, in digital forensics,it is necessary to understand the manipulating history of images. That requires to verifyall possible manipulations applied to images. Among all the image editing manipulations,recoloring is widely used to adjust or repaint the colors in images. The color informationis an important visual information that image can deliver. Thus, it is necessary toguarantee the correctness of color in digital forensics. On the other hand, many imageretouching or editing applications or software are equipped with recoloring function. Thisenables ordinary people without expertise of image processing to apply recoloring forimages. Hence, in order to secure the color information of images, in this paper, arecoloring detection method is proposed. The method is based on convolutional neuralnetwork which is quite popular in recent years. Unlike the traditional linear classifier, theproposed method can be employed for binary classification as well as multiple labelsclassification. The classification performance of different structure for the proposedarchitecture is also investigated in this paper. 相似文献