共查询到20条相似文献，搜索用时 15 毫秒
1.
Sukanta Ghosh Amar Singh Kavita N. Z. Jhanjhi Mehedi Masud Sultan Aljahdali 《计算机、材料和连续体（英文）》2022,71(3):4257-4274
Automatic plant classification through plant leaf is a classical problem in Computer Vision. Plants classification is challenging due to the introduction of new species with a similar pattern and look-a-like. Many efforts are made to automate plant classification using plant leaf, plant flower, bark, or stem. After much effort, it has been proven that leaf is the most reliable source for plant classification. But it is challenging to identify a plant with the help of leaf structure because plant leaf shows similarity in morphological variations, like sizes, textures, shapes, and venation. Therefore, it is required to normalize all plant leaves into the same size to get better performance. Convolutional Neural Networks (CNN) provides a fair amount of accuracy when leaves are classified using this approach. But the performance can be improved by classifying using the traditional approach after applying CNN. In this paper, two approaches, namely CNN + Support Vector Machine (SVM) and CNN + K-Nearest Neighbors (kNN) used on 3 datasets, namely LeafSnap dataset, Flavia Dataset, and MalayaKew Dataset. The datasets are augmented to take care all the possibilities. The assessments and correlations of the predetermined feature extractor models are given. CNN + kNN managed to reach maximum accuracy of 99.5%, 97.4%, and 80.04%, respectively, in the three datasets. 相似文献
2.
Zongshuai Liu Xuyu Xiang Jiaohua Qin Yun Tan Qin Zhang Neal N. Xiong 《计算机、材料和连续体（英文）》2021,66(1):457-466
In recent years, with the development of machine learning and deeplearning, it is possible to identify and even control crop diseases by using electronic devices instead of manual observation. In this paper, an image recognitionmethod of citrus diseases based on deep learning is proposed. We built a citrusimage dataset including six common citrus diseases. The deep learning networkis used to train and learn these images, which can effectively identify and classifycrop diseases. In the experiment, we use MobileNetV2 model as the primary network and compare it with other network models in the aspect of speed, modelsize, accuracy. Results show that our method reduces the prediction time consumption and model size while keeping a good classification accuracy. Finally,we discuss the significance of using MobileNetV2 to identify and classify agricultural diseases in mobile terminal, and put forward relevant suggestions. 相似文献
3.
Sitara Afzal Muazzam Maqsood Irfan Mehmood Muhammad Tabish Niaz Sanghyun Seo 《计算机、材料和连续体（英文）》2021,66(3):2301-2315
Cerebral Microbleeds (CMBs) are microhemorrhages caused by certain abnormalities of brain vessels. CMBs can be found in people with Traumatic Brain Injury (TBI), Alzheimer’s disease, and in old individuals having a brain injury. Current research reveals that CMBs can be highly dangerous for individuals having dementia and stroke. The CMBs seriously impact individuals’ life which makes it crucial to recognize the CMBs in its initial phase to stop deterioration and to assist individuals to have a normal life. The existing work report good results but often ignores false-positive’s perspective for this research area. In this paper, an efficient approach is presented to detect CMBs from the Susceptibility Weighted Images (SWI). The proposed framework consists of four main phases (i) making clusters of brain Magnetic Resonance Imaging (MRI) using k-mean classifier (ii) reduce false positives for better classification results (iii) discriminative feature extraction specific to CMBs (iv) classification using a five layers convolutional neural network (CNN). The proposed method is evaluated on a public dataset available for 20 subjects. The proposed system shows an accuracy of 98.9% and a 1.1% false-positive rate value. The results show the superiority of the proposed work as compared to existing states of the art methods. 相似文献
4.
Pallabi Sharma Dipankar Das Anmol Gautam Bunil Kumar Balabantaray 《International journal of imaging systems and technology》2023,33(2):495-510
The traditional process of disease diagnosis from medical images follows a manual process, which is tedious and arduous. A computer-aided diagnosis (CADs) system can work as an assistive tool to improve the diagnosis process. In this pursuit, this article introduces a unique architecture LPNet for classifying colon polyps from the colonoscopy video frames. Colon polyps are abnormal growth of cells in the colon wall. Over time, untreated colon polyps may cause colorectal cancer. Different convolutional neural networks (CNNs) based systems have been developed in recent years. However, CNN uses pooling to reduce the number of parameters and expand the receptive field. On the other hand, pooling results in data loss and is deleterious to subsequent processes. Pooling strategies based on discrete wavelet operations have been proposed in our architecture as a solution to this problem, with the promise of achieving a better trade-off between receptive field size and computing efficiency. The overall performance of this model is superior to the others, according to experimental results on a colonoscopy dataset. LPNet with bio-orthogonal wavelet achieved the highest performance with an accuracy of 93.55%. It outperforms the other state-of-the-art (SOTA) CNN models for the polyps classification task, and it is lightweight in terms of the number of learnable parameters compared with them, making the model easily deployable in edge devices. 相似文献
5.
V. Karthikeyan;M. Navin Kishore;S. Sajin; 《International journal of imaging systems and technology》2024,34(1):e23022
Kidney disease is a major health problem that affects millions of people around the world. Human kidney problems can be diagnosed with the help of computed tomography (CT), which creates cross-sectional slices of the organ. A deep end-to-end convolutional neural network (CNN) model is proposed to help radiologists detect and characterize kidney problems in CT scans of patients. This has the potential to improve diagnostic accuracy and efficiency, which in turn benefits patient care. Our strategy involves teaching a suggested deep end-to-end CNN to distinguish between healthy and diseased kidneys. The recommended CNN is trained using a standard CT image library that has been annotated to show kidney stones, cysts, and tumors. The model can then be used to detect kidney abnormalities in fresh CT scans, which may enhance the effectiveness and speed with which diagnoses are made. A total of 1812 pictures were used, each one a unique cross-sectional CT scan of the patient. Our model has a detection rate of 99.17% in CT scan validation tests. We employed a different dataset with a total of 5077 normal samples, 3709 cyst samples, 1377 stone samples, and 2283 tumor samples. In tests, our model proved to be 99.68% accurate. The suggested framework has been validated by applying it to the clinical dataset, resulting in 99% accuracy in predictions. As low-cost and portable CT scanners become more commonplace, the described concept may soon be employed outside of a hospital environment, at the point of treatment, or even in the patient's own home. 相似文献
6.
Leaf species identification leads to multitude of societal applications. There is enormous research in the lines of plant identification using pattern recognition. With the help of robust algorithms for leaf identification, rural medicine has the potential to reappear as like the previous decades. This paper discusses CNN based approaches for Indian leaf species identification from white background using smartphones. Variations of CNN models over the features like traditional shape, texture, color and venation apart from the other miniature features of uniformity of edge patterns, leaf tip, margin and other statistical features are explored for efficient leaf classification. 相似文献
7.
Suting Peng Wei Chen Jiawei Sun Boqiang Liu 《International journal of imaging systems and technology》2020,30(1):5-17
Gliomas segmentation is a critical and challenging task in surgery and treatment, and it is also the basis for subsequent evaluation of gliomas. Magnetic resonance imaging is extensively employed in diagnosing brain and nervous system abnormalities. However, brain tumor segmentation remains a challenging task, because differentiating brain tumors from normal tissues is difficult, tumor boundaries are often ambiguous and there is a high degree of variability in the shape, location, and extent of the patient. It is therefore desired to devise effective image segmentation architectures. In the past few decades, many algorithms for automatic segmentation of brain tumors have been proposed. Methods based on deep learning have achieved favorable performance for brain tumor segmentation. In this article, we propose a Multi-Scale 3D U-Nets architecture, which uses several U-net blocks to capture long-distance spatial information at different resolutions. We upsample feature maps at different resolutions to extract and utilize sufficient features, and we hypothesize that semantically similar features are easier to learn and process. In order to reduce the computational cost, we use 3D depthwise separable convolution instead of some standard 3D convolution. On BraTS 2015 testing set, we obtained dice scores of 0.85, 0.72, and 0.61 for the whole tumor, tumor core, and enhancing tumor, respectively. Our segmentation performance was competitive compared to other state-of-the-art methods. 相似文献
8.
Maoyang Zou Jinrong Hu Huan Zhang Xi Wu Jia He Zhijie Xu Yong Zhong 《计算机、材料和连续体（英文）》2019,60(2):511-525
9.
Since the web service is essential in daily lives, cyber security becomes moreand more important in this digital world. Malicious Uniform Resource Locator (URL) isa common and serious threat to cybersecurity. It hosts unsolicited content and lureunsuspecting users to become victim of scams, such as theft of private information,monetary loss, and malware installation. Thus, it is imperative to detect such threats.However, traditional approaches for malicious URLs detection that based on theblacklists are easy to be bypassed and lack the ability to detect newly generated maliciousURLs. In this paper, we propose a novel malicious URL detection method based on deeplearning model to protect against web attacks. Specifically, we firstly use auto-encoder torepresent URLs. Then, the represented URLs will be input into a proposed compositeneural network for detection. In order to evaluate the proposed system, we madeextensive experiments on HTTP CSIC2010 dataset and a dataset we collected, and theexperimental results show the effectiveness of the proposed approach. 相似文献
10.
Automated segmentation of blood vessels in retinal fundus images is essential for medical image analysis. The segmentation of retinal vessels is assumed to be essential to the progress of the decision support system for initial analysis and treatment of retinal disease. This article develops a new Grasshopper Optimization with Fuzzy Edge Detection based Retinal Blood Vessel Segmentation and Classification (GOFED-RBVSC) model. The proposed GOFED-RBVSC model initially employs contrast enhancement process. Besides, GOAFED approach is employed to detect the edges in the retinal fundus images in which the use of GOA adjusts the membership functions. The ORB (Oriented FAST and Rotated BRIEF) feature extractor is exploited to generate feature vectors. Finally, Improved Conditional Variational Auto Encoder (ICAVE) is utilized for retinal image classification, shows the novelty of the work. The performance validation of the GOFED-RBVSC model is tested using benchmark dataset, and the comparative study highlighted the betterment of the GOFED-RBVSC model over the recent approaches. 相似文献
11.
The exponential increase in data over the past few years, particularly in images, has led to more complex content since visual representation became the new norm. E-commerce and similar platforms maintain large image catalogues of their products. In image databases, searching and retrieving similar images is still a challenge, even though several image retrieval techniques have been proposed over the decade. Most of these techniques work well when querying general image databases. However, they often fail in domain-specific image databases, especially for datasets with low intraclass variance. This paper proposes a domain-specific image similarity search engine based on a fused deep learning network. The network is comprised of an improved object localization module, a classification module to narrow down search options and finally a feature extraction and similarity calculation module. The network features both an offline stage for indexing the dataset and an online stage for querying. The dataset used to evaluate the performance of the proposed network is a custom domain-specific dataset related to cosmetics packaging gathered from various online platforms. The proposed method addresses the intraclass variance problem with more precise object localization and the introduction of top result reranking based on object contours. Finally, quantitative and qualitative experiment results are presented, showing improved image similarity search performance. 相似文献
12.
目的现有的易拉罐缺陷检测系统在高速生产线中存在错检率和漏检率高,检测精度相对较低等问题,为了提高易拉罐缺陷识别的准确性,使易拉罐生产线实现进一步自动化、智能化,基于深度学习技术和迁移学习技术,提出一种适用于易拉罐制造的在线检测的算法。方法利用深度卷积网络提取易拉罐缺陷特征,通过优化卷积核,减短易拉罐缺陷检测的时间。针对国内外数据集缺乏食品包装制造的缺陷图像,构建易拉罐缺陷数据集,结合预训练网络,通过调整VGG16提升对易拉罐缺陷的识别准确率。结果对易拉罐数据集在卷积神经网络、迁移学习和调整后的预训练网络进行了易拉罐缺陷检测的性能对比,验证了基于深度学习的易拉罐缺陷检测技术在学习率为0.0005,训练10个迭代后可达到较好的识别效果,最终二分类缺陷识别率为99.7%,算法耗时119ms。结论相较于现有的易拉罐检测算法,文中提出的基于深度学习的易拉罐检测算法的识别性能更优,智能化程度更高。同时,该研究有助于制罐企业利用深度学习等AI技术促进智能化生产,减少人力成本,符合国家制造业产业升级的策略,具有一定的实际意义。 相似文献
13.
As the amount of online video content is increasing, consumers are becoming increasingly interested in various product names appearing in videos, particularly in cosmetic-product names in videos related to fashion, beauty, and style. Thus, the identification of such products by using image recognition technology may aid in the identification of current commercial trends. In this paper, we propose a two-stage deep-learning detection and classification method for cosmetic products. Specifically, variants of the YOLO network are used for detection, where the bounding box for each given input product is predicted and subsequently cropped for classification. We use four state-of-the-art classification networks, namely ResNet, InceptionResNetV2, DenseNet, and EfficientNet, and compare their performance. Furthermore, we employ dilated convolution in these networks to obtain better feature representations and improve performance. Extensive experiments demonstrate that YOLOv3 and its tiny version achieve higher speed and accuracy. Moreover, the dilated networks marginally outperform the base models, or achieve similar performance in the worst case. We conclude that the proposed method can effectively detect and classify cosmetic products. 相似文献
14.
Harshal A. Sanghvi Riki H. Patel Ankur Agarwal Shailesh Gupta Vivek Sawhney Abhijit S. Pandya 《International journal of imaging systems and technology》2023,33(1):18-38
In the present paper, our model consists of deep learning approach: DenseNet201 for detection of COVID and Pneumonia using the Chest X-ray Images. The model is a framework consisting of the modeling software which assists in Health Insurance Portability and Accountability Act Compliance which protects and secures the Protected Health Information . The need of the proposed framework in medical facilities shall give the feedback to the radiologist for detecting COVID and pneumonia though the transfer learning methods. A Graphical User Interface tool allows the technician to upload the chest X-ray Image. The software then uploads chest X-ray radiograph (CXR) to the developed detection model for the detection. Once the radiographs are processed, the radiologist shall receive the Classification of the disease which further aids them to verify the similar CXR Images and draw the conclusion. Our model consists of the dataset from Kaggle and if we observe the results, we get an accuracy of 99.1%, sensitivity of 98.5%, and specificity of 98.95%. The proposed Bio-Medical Innovation is a user-ready framework which assists the medical providers in providing the patients with the best-suited medication regimen by looking into the previous CXR Images and confirming the results. There is a motivation to design more such applications for Medical Image Analysis in the future to serve the community and improve the patient care. 相似文献
15.
目的 为快速准确识别消费者对产品意象的认知,提出一种基于分类器链的产品意象识别方法.方法 首先,构建产品意象数据集,通过相似性聚类和网络爬虫得到产品意象词与产品图像,在此基础上,进行产品意象实验,获得消费者对于产品意象的认知,构建产品意象数据集;然后,提取图像特征,利用卷积神经网络RestNet50提取产品图像特征;最后,使用分类器链算法构建产品意象识别模型,提出基于混淆矩阵与条件熵的分类器链标签顺序确定方法,确定产品意象标签顺序.结论 为了验证所述标签顺序确定方法在识别产品意象中具有优越性设计了对比实验.实验结果表明,相较于其他方法,基于分类器链的产品多标签意象识别方法考虑了标签的识别结果与相关关系,能显著提升模型对于产品多标签意象的预测性能. 相似文献
16.
Text classification has always been an increasingly crucial topic in natural language processing. Traditional text classification methods based on machine learninghave many disadvantages such as dimension explosion, data sparsity, limited generalization ability and so on. Based on deep learning text classification, this paper presents an extensive study on the text classification models including Convolutional Neural Network-Based (CNN-Based), Recurrent Neural Network-Based (RNN-based), Attention Mechanisms-Based and so on. Many studies have proved that text classification methods based on deep learning outperform the traditional methods when processing large-scale and complex datasets. The main reasons are text classification methods based on deep learning can avoid cumbersome feature extraction process and have higher prediction accuracy for a large set of unstructured data. In this paper, we also summarize the shortcomings of traditional text classification methods and introduce the text classification process based on deep learning including text preprocessing, distributed representation of text, text classification model construction based on deep learning and performance evaluation. 相似文献
17.
18.
目的 解决现有工业线束导线排序检测方法中存在的效率低、混色导线检测效果差等问题。方法 基于机器视觉技术设计一种线束导线排序检测装置,并结合图像处理技术和深度学习原理提出一种混色导线排序检测方法。首先根据线束图像中选择的感兴趣区域,分割出线束连接器图像和导线图像,并采用模板匹配和颜色定位方法完成连接器正反面的识别和单色导线的识别定位;然后采集并制作PE混色导线数据集,研究Faster R−CNN、SSD、YOLOv3和YOLOv5m等4种不同目标检测算法对PE混色导线的检测效果。结果 实验结果表明,YOLOv5m检测模型的检测速度和准确率兼顾性最好;改进系统后,检测时间减少了18.55%,平均识别准确率为98.83%。结论 改进后检测系统具有良好的检测效率和可靠性,适用于种类丰富的工业线束导线排序检测。 相似文献
19.
ZHANG Zhen;WANG He;SONG Hongxu 《测试技术学报》2024,38(4):413-419
Image classification is a basic and important direction in image processing. Since there is not only a single label value on an image, the current image classification can no longer meet people's needs, and multi-label image classification came into being. This paper proposes a multi-label image classification framework using Swin Transformer for feature extraction and a two-layer routing attention module for feature processing. Swin Transformer extracts multi-scale information through a hierarchical structure, and is superior to Vision Transformer in terms of multi-target and finer-grained image recognition. The dual-layer routing attention module enables more flexible computation allocation and content awareness. The dynamic attention mechanism adaptively adjusts the attention weight according to the characteristics of the input image, so that different positions or features can be given different levels of attention, and the intensity and range of attention can be flexibly controlled by adjusting the dynamic attention. The average precision of the model on the COCO dataset is 87.3, and the average precision on the VOC2007 dataset is 96.7, which improves the accuracy of multi-label image classification to a certain extent. 相似文献
20.
Due to the widespread usage of social media in our recent daily lifestyles, sentiment analysis becomes an important field in pattern recognition and Natural Language Processing (NLP). In this field, users’ feedback data on a specific issue are evaluated and analyzed. Detecting emotions within the text is therefore considered one of the important challenges of the current NLP research. Emotions have been widely studied in psychology and behavioral science as they are an integral part of the human nature. Emotions describe a state of mind of distinct behaviors, feelings, thoughts and experiences. The main objective of this paper is to propose a new model named BERT-CNN to detect emotions from text. This model is formed by a combination of the Bidirectional Encoder Representations from Transformer (BERT) and the Convolutional Neural networks (CNN) for textual classification. This model embraces the BERT to train the word semantic representation language model. According to the word context, the semantic vector is dynamically generated and then placed into the CNN to predict the output. Results of a comparative study proved that the BERT-CNN model overcomes the state-of-art baseline performance produced by different models in the literature using the semeval 2019 task3 dataset and ISEAR datasets. The BERT-CNN model achieves an accuracy of 94.7% and an F1-score of 94% for semeval2019 task3 dataset and an accuracy of 75.8% and an F1-score of 76% for ISEAR dataset. 相似文献