共查询到20条相似文献，搜索用时 46 毫秒
1.
Thendral Natarajan;Lakshmi Devan;Ramaprabha Palayanoor Seethapathy;Senthil Kumar Balakrishnan; 《International journal of imaging systems and technology》2024,34(2):e23036
Cervical cancer is the second most frequent cancer among women of all age groups worldwide. It occurs due to human papillomavirus. In the premature stages, the symptoms will not be predominant until they reach the final stage of cancer. Detection and classification of cervical cancer always demand gynecologists with the necessary skills and experience. The goal of the proposed work is to develop a deep learning framework to facilitate the automated classification of cervical cancer using colposcopy images. The following Deep Convolutional Neural Network (DCNN) models are proposed to detect cervical cancer and classify cervix-type images. (i) the pre-trained DCNNs, namely VGG16, ResNet50, InceptionV3, InceptionResNetV2, and ConvNeXtXLarge (ConvNeXt-XL) using Softmax classifier based on deep features; (ii) the ConvNeXt-XL model with classification using Support Vector Machine (SVM), K Nearest Neighbor (KNN) and Decision Tree (DT) based on deep features; (iii) a customized ConvNeXt-XL network to enhance the classification accuracy using serially concatenated handcrafted and deep features. The research experiment was carried out separately using two datasets: the Cervix-Type dataset (Type 1, Type 2, and Type 3) and the Real Time Cervical dataset (Normal and Abnormal). The simulation outcome confirms that the customized ConvNeXt-XL helped to improve the classification accuracy with the Cervix-Type dataset (>97%) and Real Time Cervical dataset (>98%). 相似文献
2.
Prevention of cervical cancer becomes essential and is carried out by the use of Pap smear images. Pap smear test analysis is laborious and tiresome work performed visually using a cytopathologist. Therefore, automated cervical cancer diagnosis using automated methods are necessary. This paper designs an optimal deep learning based Inception model for cervical cancer diagnosis (ODLIM-CCD) using pap smear images. The proposed ODLIM-CCD technique incorporates median filtering (MF) based pre-processing to discard the noise and Otsu model based segmentation process. Besides, deep convolutional neural network (DCNN) based Inception with Residual Network (ResNet) v2 model is utilized for deriving the feature vectors. Moreover, swallow swarm optimization (SSO) based hyperparameter tuning process is carried out for the optimal selection of hyperparameters. Finally, recurrent neural network (RNN) based classification process is done to determine the presence of cervical cancer or not. In order to showcase the improved diagnostic performance of the ODLIM-CCD technique, a series of simulations occur on benchmark test images and the outcomes highlighted the improved performance over the recent approaches with a superior accuracy of 0.9661. 相似文献
3.
Zijun Sha Lin Hu Babak Daneshvar Rouyendegh 《International journal of imaging systems and technology》2020,30(2):495-506
Breast cancer is caused by the abnormal and rapid growth of breast cells. An early diagnosis can ensure an easier and effective treatment. A mass in the breast is a significant early sign of breast cancer, even though differentiating the cancerous mass's tissue from normal tissue for diagnosis is a difficult task for radiologists. The development of computer-aided detection systems in recent years has led to nondestructive and efficient cancer diagnostic techniques. This paper proposes a comprehensive method to locate the cancerous region in the mammogram image. This method employs image noise reduction, optimal image segmentation based on the convolutional neural network, a grasshopper optimization algorithm, and optimized feature extraction and feature selection based on the grasshopper optimization algorithm, thereby improving precision and decreasing the computational cost. This method was applied to the Mammographic Image Analysis Society Digital Mammogram Database and Digital Database for Screening Mammography breast cancer databases and the simulation results were compared with 10 different state-of-the-art methods to analyze the proposed system's efficiency. Final results showed that the proposed method had 96% Sensitivity, 93% Specificity, 85% PPV, 97% NPV, 92% accuracy, and better efficiency than other traditional methods in terms of Sensitivity, Specificity, PPV, NPV, and Accuracy. 相似文献
4.
OCT视网膜图像中存在着噪声和散斑,单一的提取空间特征往往容易遗漏一些重要信息,导致不能准确地分割目标区域。而OCT图像本身存在光谱频域特征,针对OCT图像的频域特征,本文基于U-Net和快速傅立叶卷积提出一种新的双编码器模型以提高对OCT图像视网膜层、液体的分割性能,提出的频域编码器可以提取图像频域信息并通过快速傅里叶卷积转换为空间信息,将很好地弥补单一空间编码器遗漏特征信息的不足。经过与其他经典模型的对比和消融实验,结果表明,随着频域编码器的添加,该模型能有效提升对视网膜层和液体的分割性能,平均Dice系数和mIoU相较于U-Net均提高2%,相较于ReLayNet分别提高8%和4%,其中对液体的分割提升尤为明显,相较于U-Net 模型Dice系数提高了10%。
相似文献5.
目的 针对锂电池极片涂布缺陷种类多,传统方法分类检测精度不高,以及人工依赖性强等问题,提出一种基于卷积神经网络的锂电池极片涂布缺陷自动分类算法。方法 首先对网络结构以及模型参数进行优化,接着在网络中加入跳跃连接结构,将空洞卷积提取到的多尺度特征与高层特征进行融合以获取更多缺陷特征,并采用LeakyReLU(Leaky Rectified Linear Unit)激活函数保留图像中的负值特征信息,最后通过构建的数据集训练模型,实现锂电池极片涂布缺陷的准确分类。结果 实验结果表明,当前方法识别准确率能够达到99.34%,平均检测时间为51ms。结论 改进后的方法能够准确分类出锂电池极片18种涂布缺陷,满足工业生产中实时分类检测的要求。 相似文献
6.
Guoqing Liu;Yu Guo;Qiyu Jin;Guoqing Chen;Barintag Saheya;Caiying Wu; 《International journal of imaging systems and technology》2024,34(3):e23090
Skin lesion segmentation is a crucial step for skin lesion analysis and subsequent treatment. However, it is still a challenging task due to the irregular and fuzzy lesion borders, and the diversity of skin lesions. In this article, we propose Triple-UNet, an organic combination of three UNet architectures with suitable modules, to automatically segment skin lesions. To enhance the target object region of the image, we design a region of interest enhancement module (ROIE) that uses the predicted score map of the first UNet. The enhanced image and the features learned by the first UNet help the second UNet obtain a better score map. Finally, the results are fine-tuned by the third UNet. We evaluate our algorithm on a publicly available dataset of skin lesion segmentation. Experiments have shown that TripleUNet achieves an accuracy of 92.5% on the ISIC-2018 skin lesion segmentation benchmark, with Dice and mIoU of 0.909 and 0.836, respectively, which outperforms the state-of-the-art algorithms. 相似文献
7.
Phong Thanh Nguyen Vy Dang Bich Huynh Khoa Dang Vo Phuong Thanh Phan Eunmok Yang Gyanendra Prasad Joshi 《计算机、材料和连续体（英文）》2021,66(3):2815-2830
Diabetic Retinopathy (DR) is a significant blinding disease that poses serious threat to human vision rapidly. Classification and severity grading of DR are difficult processes to accomplish. Traditionally, it depends on ophthalmoscopically-visible symptoms of growing severity, which is then ranked in a stepwise scale from no retinopathy to various levels of DR severity. This paper presents an ensemble of Orthogonal Learning Particle Swarm Optimization (OPSO) algorithm-based Convolutional Neural Network (CNN) Model EOPSO-CNN in order to perform DR detection and grading. The proposed EOPSO-CNN model involves three main processes such as preprocessing, feature extraction, and classification. The proposed model initially involves preprocessing stage which removes the presence of noise in the input image. Then, the watershed algorithm is applied to segment the preprocessed images. Followed by, feature extraction takes place by leveraging EOPSO-CNN model. Finally, the extracted feature vectors are provided to a Decision Tree (DT) classifier to classify the DR images. The study experiments were carried out using Messidor DR Dataset and the results showed an extraordinary performance by the proposed method over compared methods in a considerable way. The simulation outcome offered the maximum classification with accuracy, sensitivity, and specificity values being 98.47%, 96.43%, and 99.02% respectively. 相似文献
8.
Xuanlong He;Hong Yang;Jipan Xu;Hongbo Mu; 《International journal of imaging systems and technology》2024,34(3):e23107
COVID-19 has been ravaging the world for a long time, and although its effects are currently the same as those of a cold or a fever, timely diagnosis of COVID-19 in the elderly and in patients with related illnesses is still a matter of great urgency. To address this challenge, we propose a model that combines the strengths of the Swin Transformer and ResNet34 architectures to efficiently diagnose COVID-19 in elderly and vulnerable patients. In this paper, we design a model that integrates Swin transformer and resnet34, which not only integrates the advantages of transformer and CNN but also achieves excellent performance in this image classification problem. Moreover, a pre-processing method is also proposed to increase the accuracy of the model to 99.08%. In this paper, experiments were conducted on Kaggle's publicly available three-classification and four-classification datasets, respectively, and on the three main evaluation metrics of Accuracy, Precision, and Recall, the first dataset obtained 98.81%, 99.49%, and 97.99%, while the second dataset obtained 88.82%, 88.92%, and 86.38%. These findings highlight the validity and potential of our proposed model for diagnosing the presence or absence of COVID-19 in elderly and vulnerable patients. 相似文献
9.
With the development of artificial intelligence-related technologies such as deep learning, various organizations, including the government, are making various efforts to generate and manage big data for use in artificial intelligence. However, it is difficult to acquire big data due to various social problems and restrictions such as personal information leakage. There are many problems in introducing technology in fields that do not have enough training data necessary to apply deep learning technology. Therefore, this study proposes a mixed contour data augmentation technique, which is a data augmentation technique using contour images, to solve a problem caused by a lack of data. ResNet, a famous convolutional neural network (CNN) architecture, and CIFAR-10, a benchmark data set, are used for experimental performance evaluation to prove the superiority of the proposed method. And to prove that high performance improvement can be achieved even with a small training dataset, the ratio of the training dataset was divided into 70%, 50%, and 30% for comparative analysis. As a result of applying the mixed contour data augmentation technique, it was possible to achieve a classification accuracy improvement of up to 4.64% and high accuracy even with a small amount of data set. In addition, it is expected that the mixed contour data augmentation technique can be applied in various fields by proving the excellence of the proposed data augmentation technique using benchmark datasets. 相似文献
10.
Yong Luo Xiaojie Li Chao Luo Feng Wang Xi Wu Imran Mumtaz Cheng Yi 《计算机、材料和连续体（英文）》2020,65(2):1771-1780
Tissue segmentation is a fundamental and important task in nasopharyngeal images analysis. However, it is a challenging task to accurately and quickly segment various tissues in the nasopharynx region due to the small difference in gray value between tissues in the nasopharyngeal image and the complexity of the tissue structure. In this paper, we propose a novel tissue segmentation approach based on a two-stage learning framework and U-Net. In the proposed methodology, the network consists of two segmentation modules. The first module performs rough segmentation and the second module performs accurate segmentation. Considering the training time and the limitation of computing resources, the structure of the second module is simpler and the number of network layers is less. In addition, our segmentation module is based on U-Net and incorporates a skip structure, which can make full use of the original features of the data and avoid feature loss. We evaluated our proposed method on the nasopharyngeal dataset provided by West China Hospital of Sichuan University. The experimental results show that the proposed method is superior to many standard segmentation structures and the recently proposed nasopharyngeal tissue segmentation method, and can be easily generalized across different tissue types in various organs. 相似文献
11.
Skin cancer (melanoma) is one of the most aggressive of the cancers and the prevalence has significantly increased due to increased exposure to ultraviolet radiation. Therefore, timely detection and management of the lesion is a critical consideration in order to improve lifestyle and reduce mortality. To this end, we have designed, implemented and analyzed a hybrid approach entailing convolutional neural networks (CNN) and local binary patterns (LBP). The experiments have been performed on publicly accessible datasets ISIC 2017, 2018 and 2019 (HAM10000) with data augmentation for in-distribution generalization. As a novel contribution, the CNN architecture is enhanced with an intelligible layer, LBP, that extracts the pertinent visual patterns. Classification of Basal Cell Carcinoma, Actinic Keratosis, Melanoma and Squamous Cell Carcinoma has been evaluated on 8035 and 3494 cases for training and testing, respectively. Experimental outcomes with cross-validation depict a plausible performance with an average accuracy of 97.29%, sensitivity of 95.63% and specificity of 97.90%. Hence, the proposed approach can be used in research and clinical settings to provide second opinions, closely approximating experts’ intuition. 相似文献
12.
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
13.
Balakumaresan Ragupathy Manivannan Karunakaran 《International journal of imaging systems and technology》2021,31(1):118-127
In medical imaging, segmenting brain tumor becomes a vital task, and it provides a way for early diagnosis and treatment. Manual segmentation of brain tumor in magnetic resonance (MR) images is a time‐consuming and challenging task. Hence, there is a need for a computer‐aided brain tumor segmentation approach. Using deep learning algorithms, a robust brain tumor segmentation approach is implemented by integrating convolution neural network (CNN) and multiple kernel K means clustering (MKKMC). In this proposed CNN‐MKKMC approach, classification of MR images into normal and abnormal is performed by CNN algorithm. At next, MKKMC algorithm is employed to segment the brain tumor from the abnormal brain image. The proposed CNN‐MKKMC algorithm is evaluated both visually and objectively in terms of accuracy, sensitivity, and specificity with the existing segmentation methods. The experimental results demonstrate that the proposed CNN‐MKKMC approach yields better accuracy in segmenting brain tumor with less time cost. 相似文献
14.
Diabetic retinopathy (DR) is an important cause of blindness. If not diagnosed and treated in a timely manner, it can lead to irreversible vision loss. The diagnosis of DR relies heavily on specialized ophthalmologists. In recent years, with the development of artificial intelligence a number of diagnostics using this technique have begun to appear. One method for diagnosing diseases in this field is to segment four common kinds of lesions from color fundus images, including: exudates (EX), soft exudates (SE), hemorrhages (HE), and microaneurysms (MA). In this paper, we propose a segmentation model for DR based on deep learning. The main part of the model consists of two layers of improved U-Net network based on transformer, corresponding to the two stages of coarse segmentation and fine segmentation, respectively. The model can segment four common kinds of lesions from the input color fundus image at the same time. To validate the performance of our proposed model, we test our model on three public datasets: IDRiD, DDR, and DIARETDB1. The test results show that our proposed model achieves competitive results compared with the existing methods in terms of PR-AUC, ROC-AUC, Dice, and IoU, especially for lesions segmentation of SE and MA. 相似文献
15.
目的研究利用深度学习辅以拉普拉斯金字塔来完成图像压缩与重构。方法利用卷积神经网络提取图像的主要特征,利用双三线性插值法来减少特征尺寸,使用拉普拉斯金字塔来构建分层体系,从而逐步地减少图像大小以达到压缩的目的。在重构端上,对此系统则进行卷积操作,并采用上采样过程,进行图像的恢复重构过程,得到重构图。结果采用来自法国贝尔实验室的set 5与set 14数据集进行验证,使用2层金字塔即在16倍的高倍率压缩下进行实验结果验证,结果表明在主观评价上使用深度学习的方法在清晰度和还原度上要优于PCA,DCT和SVD,同时在客观评价上文中方法取得了标准差(52.73)与信息熵(7.44)的最好结果,高于PCA的49.70与7.38。SVD变换法与DCT变换法,在标准差上只有48.69和49.02,远不如文中方法,同时图片的信息熵只有7.34与7.35,低于文中的7.44。结论利用拉普拉斯金字塔结构来设计卷积神经网络结构来完成图像压缩与重构取得了不错的效果。 相似文献
16.
视频图像中的小像素目标难以检测。针对城市道路视频中的小像素目标,本文提出了一种改进YOLOv3的卷积神经网络Road_Net检测方法。首先,基于改进的YOLOv3,设计了一种新的卷积神经网络Road_Net;其次,针对小像素目标检测更依赖于浅层特征,采用了4个尺度检测方法。最后,结合改进的M-Softer-NMS算法来进一步提高图像中目标的检测精度。为了验证所提出算法的有效性,本文收集并标注了用于城市道路小像素目标物体检测的数据集Road-garbage Dataset,实验结果表明,本文算法能有效地检测出诸如纸屑、石块等在视频中相对于路面的较小像素目标。 相似文献
17.
Vehicle type classification is considered a central part of an intelligent traffic system. In recent years, deep learning had a vital role in object detection in many computer vision tasks. To learn high-level deep features and semantics, deep learning offers powerful tools to address problems in traditional architectures of handcrafted feature-extraction techniques. Unlike other algorithms using handcrated visual features, convolutional neural network is able to automatically learn good features of vehicle type classification. This study develops an optimized automatic surveillance and auditing system to detect and classify vehicles of different categories. Transfer learning is used to quickly learn the features by recording a small number of training images from vehicle frontal view images. The proposed system employs extensive data-augmentation techniques for effective training while avoiding the problem of data shortage. In order to capture rich and discriminative information of vehicles, the convolutional neural network is fine-tuned for the classification of vehicle types using the augmented data. The network extracts the feature maps from the entire dataset and generates a label for each object (vehicle) in an image, which can help in vehicle-type detection and classification. Experimental results on a public dataset and our own dataset demonstrated that the proposed method is quite effective in detection and classification of different types of vehicles. The experimental results show that the proposed model achieves 96.04% accuracy on vehicle type classification. 相似文献
18.
目的研究无需进行复杂的图像预处理和人工特征提取,就能提高光学遥感图像的船只检测准确率和实现船只类型精细分类。方法对输入的检测图像,采用选择性搜索的方法产生船只候选区域,用已经标记好的训练样本对卷积神经网络进行监督训练,得到网络参数,然后使用经过监督训练的卷积神经网络提取抽象特征,并对候选区域进行分类,根据船只候选区域的分类概率同时确定船只的位置以及类型。结果与现有的2种检测方法进行对比,实验结果表明卷积神经网络能有效提高船只检测准确率,平均检测准确率达到了93.3%。结论该检测方法无需进行复杂的预处理,能同时对船只进行检测和分类,并能有效提高船只检测准确率。 相似文献
19.
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
20.
目的 进一步提高图像复原的性能.方法 提出一种基于隐式知识迁移(Implicit knowledge transfer)和显式掩码引导(Explicit mask guide)的图像复原通用方法IECNN.将一般的图像复原任务明确拆分为退化区域定位和区域引导复原等 2 个阶段.首先利用掩码预测网络中固有的退化定位知识,并进行训练,检测严重退化区域,然后提出一种自适应的注意力知识蒸馏方法,将退化区域知识隐式迁移到复原网络中,且无须任何额外的推理计算,随后提出一种掩码引导下的 2 种模块,在扩充全局感受野的同时重点关注退化区域,以此显式进行图像复原.结果 在进行消融实验时,通过可视化特征图与成对关系图直观展现了各个组件的有效性.为了证明文中方法的通用性,在 4 种空间变化的图像复原任务中,以峰值信噪比(Peak signal to noise ratio)和结构相似性(Structural similarity)2 个指标与其他基准方法进行了定量比较,在视觉效果上进行了定性比较.结论 证明了隐式知识迁移和显式掩码引导对于图像复原的有效性. 相似文献