检索结果-内蒙古大学图书馆

Big data analytics enabled deep convolutional neural network for the diagnosis of cancer

KNOWLEDGE AND INFORMATION SYSTEMS 2024年第2期66卷 905-931页

作者： Awotunde, Joseph Bamidele Panigrahi, Ranjit Shukla, Shubham Panda, Baidyanath Bhoi, Akash Kumar Univ Ilorin Fac Informat & Commun Sci Dept Comp Sci Ilorin 240003 Kwara State Nigeria Sikkim Manipal Univ Sikkim Manipal Inst Technol Dept Comp Applicat Majitar 737136 Sikkim India KIET Grp Inst Elect & Commun Engn Delhi NCR Ghaziabad 201206 India LTIMindtree 1 Amer Row3 Rd Floor Hartford CT 06103 USA Sikkim Manipal Univ Res Directorate Gangtok 737102 Sikkim India

Artificial intelligence (AI) has been shown to be a formidable instrument in managing Big Healthcare Data, and it has seen considerable success in bioinformatics. The advancement of big data in biological sciences has given rise to big data analytics (BDA) and artificial intelligence (AI). Because the AI methodologies used in bioinformatics are parallel and iterative, scalable big data management employing distributed and parallel technology is possible. The growth of bioinformatics has resulted in significant storage and administration issues;to share information, such large amounts of data must be handled efficiently. Computational developments in information technology have enabled analytical systems to cope with such data. Therefore, this study emphasizes the impact of big data and BDA in bioinformatics. A practical use of BDAs and AI in cancer classification was given, combining a unique Analysis of Variance (ANOVA) approach with Ant Colony Optimization (ACO) as a hybrid feature selection to pick significant genes while minimizing gene redundancy. deep convolutional neural networks (DCNN) were employed to classify the datasets. It is because microarray data are produced from gene expression data, and it frequently has a limited number of samples but a huge feature collection size. Using the same datasets, the suggested system outperformed earlier state-of-the-art approaches. The results of the proposed model on all the Leukemia, DLBCL, Colon, and SRDCT datasets revealed an average classification accuracy of 97.7%, 99.9%, 99.9% and 100%, respectively.

关键词： Bioinformatics Big data analytics Artificial intelligence deep convolutional neural network Ant colony optimization Analysis of variance Breast cancer

来源：评论

学校读者我要写书评

暂无评论

Shrinkage Crack Detection in Expansive Soil using deep convolutional neural network and Transfer Learning

引用

KSCE JOURNAL OF CIVIL ENGINEERING 2022年第10期26卷 4202-4213页

作者： Andrushia, A. Diana Neebha, T. Mary Umadevi, S. Anand, N. Cashell, Katherine A. Karunya Inst Technol & Sci Dept ECE Coimbatore 641114 Tamil Nadu India VIT Univ Ctr Nano Elect & VLSI Design SENSE Chennai 632014 Tamil Nadu India Karunya Inst Technol & Sci Dept Civil Engn Coimbatore 641114 Tamil Nadu India Brunel Univ London Dept Civil & Environm Engn Uxbridge UB8 3PH Middx England

The formation of shrinkage cracks is a natural phenomenon in expansive soils. The development of these cracks affects both the physical and mechanical properties of the soil. This paper proposes new procedures for predicting and detecting the formation of crack patterns in expansive soils, based on customized Convolution neural network (CNN) and transfer learning. A total of four different deep learning models are developed to detect the soil crack pattern by changing the convolution layers and hyper-parameters in the analysis. The novelty of the proposed detection methods lies in the use of customized CNN models in shrinkage crack detection for expansive soils. The customized CNN models are constructed by varying the number of convolution layers and the hyperparameters. The results show that the proposed CNN models provide very accurate results and are capable of detecting the presence of cracks in the soil with great accuracy. The best results are from one of the customized CNN models namely the Customized CNN Model 2 which consists of five convolution layers, three activation layers, one pooling layer, two fully connected layers, and a softmax layer. The results from this model are compared with other well-known approaches from the literature and are shown to provide improved results. Overall, the proposed deep learning methods developed in this paper produce excellent results in terms of the accurate detection of shrinkage soil cracks and can also be applied to other types of soil cracks.

关键词： Shrinkage crack Crack detection Expansive soil deep learning deep convolutional neural network

来源：评论

学校读者我要写书评

暂无评论

Video super-resolution reconstruction based on deep convolutional neural network and spatio-temporal similarity

引用

The Journal of China Universities of Posts and Telecommunications 2016年第5期23卷 68-81页

作者： Li Linghui Du Junping Liang Meiyu Ren Nan Fan Dan Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia Beijing University of Posts and Telecommunications School of Computer Science Beijing University of Posts and Telecommunications

Existing learning-based super-resolution （SR） reconstruction algorithms are mainly designed for single image, which ignore the spatio-temporal relationship between video frames. Aiming at applying the advantages of learning-based algorithms to video SR field, a novel video SR reconstruction algorithm based on deep convolutional neural network （CNN） and spatio-temporal similarity （STCNN-SR） was proposed in this paper. It is a deep learning method for video SR reconstruction, which considers not onlv the mapping relationship among associated low-resolution （LR） and high-resolution （HR） image blocks, but also the spatio-temporal non-local complementary and redundant information between adjacent low-resolution video frames. The reconstruction speed can be improved obviously with the pre-trained end-to-end reconstructed coefficients. Moreover, the performance of video SR will be further improved by the optimization process with spatio-temporal similarity. Experimental results demonstrated that the proposed algorithm achieves a competitive SR quality on both subjective and objective evaluations, when compared to other state-of-the-art algorithms.

关键词： video SR reconstruction deep convolutional neural network spatio-temporal siruilarity Zernike moment feature

来源：评论

学校读者我要写书评

暂无评论

Classification of Breast Abnormalities Using a deep convolutional neural network and Transfer Learning

引用

JOURNAL OF COMMUNICATIONS TECHNOLOGY AND ELECTRONICS 2021年第6期66卷 778-783页

作者： Ruchai, A. N. Kober, V., I Dorofeev, K. A. Karnaukhov, V. N. Mozerov, M. G. Chelyabinsk State Univ Chelyabinsk 454001 Russia Kharkevich Inst Inst Informat Transmiss Problems Moscow 127051 Russia South Ural State Univ Chelyabinsk 454080 Russia Ctr Sci Res & Higher Educ Ensenada 22860 Baja California Mexico

A new algorithm for classification of breast pathologies in digital mammography using a convolutional neural network and transfer learning is proposed. The following pretrained neural networks were chosen: MobileNetV2, InceptionResNetV2, Xception, and ResNetV2. All mammographic images were pre-processed to improve classification reliability. Transfer training was carried out using additional data augmentation and fine-tuning. The performance of the proposed algorithm for classification of breast pathologies in terms of accuracy on real data is discussed and compared with that of state-of-the-art algorithms on the available MIAS database.

关键词： Keywords classification abnormalities digital mammography deep convolutional neural network transfer learning data augmentation fine-tuning

来源：评论

学校读者我要写书评

暂无评论

Dental Caries Detection Using Score-Based Multi-Input deep convolutional neural network

引用

IEEE ACCESS 2022年 10卷 18320-18329页

作者： Imak, Andac Celebi, Adalet Siddique, Kamran Turkoglu, Muammer Sengur, Abdulkadir Salam, Iftekhar Munzur Univ Fac Engn Dept Elect & Elect Engn TR-62000 Tunceli Turkey Bingol Univ Fac Dent Oral & Maxillofacial Surg Dept TR-12000 Bingol Turkey Xiamen Univ Malaysia Sch Elect & Comp Engn Dept Informat & Commun Technol Sepang 43900 Malaysia Samsun Univ Fac Engn Dept Software Engn TR-55000 Samsun Turkey Firat Univ Fac Technol Dept Elect & Elect Engn TR-23100 Elazig Turkey

Panoramic and periapical radiograph tools help dentists in diagnosing the most common dental diseases, such as dental caries. Generally, dental caries is manually diagnosed by dentists based on panoramic and periapical images. For several reasons, such as carelessness caused by heavy workload and inexperience, manual diagnosis may cause unnoticeable dental caries. Thus, computer-based intelligent vision systems supported by machine learning and image processing techniques are needed to prevent these negativities. This study proposed a novel approach for the automatic diagnosis of dental caries based on periapical images. The proposed procedure used a multi-input deep convolutional neural network ensemble (MI-DCNNE) model. Specifically, a score-based ensemble scheme was employed to increase the achievement of the proposed MI-DCNNE method. The inputs to the proposed approach were both raw periapical images and an enhanced form of it. The score fusion was carried out in the Softmax layer of the proposed multi-input CNN architecture. In the experimental works, a periapical image dataset (340 images) covering both caries and non-caries images were used for the performance evaluation of the proposed method. According to the results, it was seen that the proposed model is quite successful in the diagnosis of dental caries. The reported accuracy score is 99.13%. This result shows that the proposed MI-DCNNE model can effectively contribute to the classification of dental caries.

关键词： Dentistry convolutional neural networks X-ray imaging Teeth Feature extraction Diseases Training Dental caries detection score-based fusion deep convolutional neural network classification periapical images

来源：评论

学校读者我要写书评

暂无评论

Singular Value Manipulating: An Effective DRL-Based Adversarial Attack on deep convolutional neural network

引用

neural PROCESSING LETTERS 2023年第9期55卷 12459-12480页

作者： He, Shuai Fu, Cai Feng, Guanyun Lv, Jianqiang Deng, Fengyang Huazhong Univ Sci & Technol Hubei Engn Res Ctr Big Data Secur Sch Cyber Sci & Engn Hubei Key Lab Distributed Syst Secur Wuhan 430000 Hubei Peoples R China

In recent years, deep convolutional neural networks (DCNNs) have become increasingly prevalent in image processing applications. However, DCNNs are vulnerable to adversarial attacks, which are generated by adding imperceptible perturbations to the input that can cause the network to misclassify the image. In this study, we propose a black-box transferable adversarial attack method. The goal is to enhance the understanding of the vulnerability of these networks. Meanwhile, it could help develop more robust defenses against such attacks. This attack efficiently generates adversarial examples by manipulating the singular value matrix instead of directly perturbing pixels with complex noise. We utilize soft actor-critic to explore an optimal perturbation strategy. We perform extensive evaluations with VOC 2012, MS Coco 2017 datasets on object detection models, the MNIST dataset on image classification models, as well as the TT-100K dataset on a real-world case study to evaluate the proposed singular value manipulating attack (SVMA). Comparison results demonstrate that SVMA achieves a consistent query efficiency and attack ability on both one-stage detector Yolo and two-stage detector Faster R-CNN. Additionally, our case study demonstrates the adversarial examples of SVMA are effective in real-world scenarios. In the end, we propose a defense against such attacks.

关键词： Black-box adversarial attack Object detection deep convolutional neural network Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Twin Identification over Viewpoint Change: A deep convolutional neural network Surpasses Humans

引用

ACM TRANSACTIONS ON APPLIED PERCEPTION 2023年第3期20卷 1-15页

作者： Parde, Connor J. Strehle, Virginia E. Banerjee, Vivekjyoti Hu, Ying Cavazos, Jacqueline G. Castillo, Carlos D. O'Toole, Alice J. Univ Texas Dallas Sch Behav & Brain Sci 800 Campbell RdMail Stop GR41 Richardson TX 75080 USA Univ Maryland Inst Adv Comp Studies 8125 Paint Branch Dr College Pk MD 20740 USA Univ Calif Irvine Sch Educ 401 Peltason DrSuite 3200 Irvine CA 92617 USA Johns Hopkins Univ Whiting Sch Engn 3400 N Charles St Baltimore MD 21218 USA

deep convolutional neural networks (DCNNs) have achieved human-level accuracy in face identification (Phillips et al., 2018), though it is unclear how accurately they discriminate highly-similar faces. Here, humans and a DCNN performed a challenging face-identity matching task that included identical twins. Participants (N = 87) viewed pairs of face images of three types: same-identity, general imposters (different identities from similar demographic groups), and twin imposters (identical twin siblings). The task was to determine whether the pairs showed the same person or different people. Identity comparisons were tested in three viewpoint-disparity conditions: frontal to frontal, frontal to 45 degrees profile, and frontal to 90 degrees profile. Accuracy for discriminating matched-identity pairs from twin-imposter pairs and general-imposter pairs was assessed in each viewpoint-disparity condition. Humans were more accurate for general-imposter pairs than twin-imposter pairs, and accuracy declined with increased viewpoint disparity between the images in a pair. A DCNN trained for face identification (Ranjan et al., 2018) was tested on the same image pairs presented to humans. Machine performance mirrored the pattern of human accuracy, but with performance at or above all humans in all but one condition. Human and machine similarity scores were compared across all image-pair types. This item-level analysis showed that human and machine similarity ratings correlated significantly in six of nine image-pair types [range r = 0.38 to r = 0.63], suggesting general accord between the perception of face similarity by humans and the DCNN. These findings also contribute to our understanding of DCNN performance for discriminating high-resemblance faces, demonstrate that the DCNN performs at a level at or above humans, and suggest a degree of parity between the features used by humans and the DCNN.

关键词： Face recognition deep convolutional neural network human face recognition human-machine comparison

来源：评论

学校读者我要写书评

暂无评论

Wire segmentation for printed circuit board using deep convolutional neural network and graph cut model

引用

IET IMAGE PROCESSING 2018年第5期12卷 793-800页

作者： Qiao, Kai Zeng, Lei Chen, Jian Hai, Jinjin Yan, Bin Natl Digital Switching Syst Engn & Technol Res Ct Zhengzhou Henan Peoples R China

Printed circuit board wire segmentation based on computed tomography (CT) image can help subsequently locate and estimate inner faults of circuit in an automatic and non-destructive manner. However, CT imaging is prone to suffer from scattered artefacts, metal artefacts and other interference, destroying compact boundary structures of wires. Wires have the characteristic of dense local distribution, and massive vias, pads, and coppers can appear close to wires, resulting in mazy recognition surroundings. The above-mentioned problems bring great difficulty for high-accuracy recognition and location of wire segmentation. In this study, considering that deep convolutional neural network (DCNN) with powerful feature representation can recognise wires in confused surroundings, and graph cut (GC) model relying on grayscale and local texture information specialises in protecting edge structures of wires, the authors propose an effective framework called DCNN-GC that employs DCNN to obtain global semantic prior to guide the GC model to accomplish satisfactory wire segmentation. The authors qualitative and quantitative results demonstrate outstanding performance, and achieve overwhelming intersection over union compared with traditional and DCNN-based methods.

关键词： wires (electric) image segmentation printed circuits neural nets circuit analysis computing image representation image recognition graph theory image texture wire segmentation printed circuit board deep convolutional neural network graph cut model computed tomography image CT images inner fault location inner fault estimation scattered artefacts metal artefacts compact boundary structures dense local distribution massive vias pads high-accuracy recognition DCNN feature representation GC model local texture information grayscale information edge structure protection global semantic prior

来源：评论

学校读者我要写书评

暂无评论

Computer-Aided Diagnosis Scheme for Distinguishing Between Benign and Malignant Masses on Breast DCE-MRI Images Using deep convolutional neural network with Bayesian Optimization

引用

JOURNAL OF DIGITAL IMAGING 2021年第1期34卷 116-123页

作者： Hizukuri, Akiyoshi Nakayama, Ryohei Nara, Mayumi Suzuki, Megumi Namba, Kiyoshi Ritsumeikan Univ Dept Elect & Comp Engn 1-1-1 Noji Higashi Kusatsu Shiga 5258577 Japan Hokuto Hosp Dept Breast Surg 7-5 KisenInada Cho Obihiro Hokkaido 0800833 Japan

Although magnetic resonance imaging (MRI) has a higher sensitivity of early breast cancer than mammography, the specificity is lower. The purpose of this study was to develop a computer-aided diagnosis (CAD) scheme for distinguishing between benign and malignant breast masses on dynamic contrast material-enhanced MRI (DCE-MRI) by using a deep convolutional neural network (DCNN) with Bayesian optimization. Our database consisted of 56 DCE-MRI examinations for 56 patients, each of which contained five sequential phase images. It included 26 benign and 30 malignant masses. In this study, we first determined a baseline DCNN model from well-known DCNN models in terms of classification performance. The optimum architecture of the DCNN model was determined by changing the hyperparameters of the baseline DCNN model such as the number of layers, the filter size, and the number of filters using Bayesian optimization. As the input of the proposed DCNN model, rectangular regions of interest which include an entire mass were selected from each of DCE-MRI images by an experienced radiologist. Three-fold cross validation method was used for training and testing of the proposed DCNN model. The classification accuracy, the sensitivity, the specificity, the positive predictive value, and the negative predictive value were 92.9% (52/56), 93.3% (28/30), 92.3% (24/26), 93.3% (28/30), and 92.3% (24/26), respectively. These results were substantially greater than those with the conventional method based on handcrafted features and a classifier. The proposed DCNN model achieved high classification performance and would be useful in differential diagnoses of masses in breast DCE-MRI images as a diagnostic aid.

关键词： Breast magnetic resonance imaging Mass deep convolutional neural network Bayesian optimization Computer-aided diagnosis

来源：评论

学校读者我要写书评

暂无评论

Assessing comparative importance of DNA sequence and epigenetic modifications on gene expression using a deep convolutional neural network

引用

COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL 2022年 20卷 3814-3823页

作者： Gao, Shang Rehman, Jalees Dai, Yang Univ Illinois Dept Biomed Engn Chicago IL 60607 USA Univ Illinois Dept Med Div Cardiol Chicago IL USA Univ Illinois Dept Biochem & Mol Genet Chicago IL USA Univ Illinois Canc Ctr Chicago IL USA

Gene expression is regulated at both transcriptional and post-transcriptional levels. DNA sequence and epigenetic modifications are key factors which regulate gene transcription. Understanding their complex interactions and their respective contributions to gene expression regulation remains a challenge in biological studies. We have developed iSEGnet, a framework of deep convolutional neural network to predict mRNA abundance using the information on DNA sequences as well as epigenetic modifications within genes and their cis-regulatory regions. We demonstrate that our framework outperforms other machine learning models in terms of predicting mRNA abundance using transcriptional and epigenetic profiles from six distinct cell lines/types chosen from the ENCODE. The analysis from the learned models also reveals that specific regions around promotors and transcription termination sites are most important for gene expression regulation. Using the method of Integrated Gradients, we identify narrow segments in these regions which are most likely to impact gene expression for a specific epigenetic modification. We further show that these identified segments are enriched in known active regulatory regions by comparing the transcription factor binding sites obtained via ChIP-seq. Moreover, we demonstrate how iSEGnet can uncover potential transcription factors that have regulatory functions in cancer using two cancer multi-omics data. Published by Elsevier B.V. on behalf of Research network of Computational and Structural Biotechnology.

关键词： Epigenetic modifications DNA sequence Transcription factors Multi-omics deep convolutional neural network deep learning Prediction of gene expression Integrated gradients

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：