International Journal of Image, Graphics and Signal Processing最新文献_第6页

Level Set Segmentation of Images using Block Matching Local SVD Operator based Sparsity and TV Regularization 基于稀疏性和TV正则化的块匹配局部SVD算子图像水平集分割

International Journal of Image, Graphics and Signal Processing Pub Date : 2023-04-08 DOI: 10.5815/ijigsp.2023.02.05

K. Ramudu, Gajula Laxmi Bhavani, Manabolu Nishanth, Akula Prakash Raj, Vamshika Analdas

引用次数: 1

Linear Discriminate Analysis based Robust Watermarking in DWT and LWT Domain with PCA based Statistical Feature Reduction 基于线性判别分析的DWT和LWT域鲁棒水印与基于PCA的统计特征约简

International Journal of Image, Graphics and Signal Processing Pub Date : 2023-04-08 DOI: 10.5815/ijigsp.2023.02.07

Sushma Jaiswal, Manoj Kumar Pandey

引用次数: 0

Tropical Rain Intensity Impact on Raindrop Diameter and Specific Signal Attenuation at Microwaves Communication Link 热带雨强度对微波通信链路上雨滴直径和特定信号衰减的影响

International Journal of Image, Graphics and Signal Processing Pub Date : 2023-04-08 DOI: 10.5815/ijigsp.2023.02.06

Isabona Joseph, Ibrahim Habibat Ojochogwu, I. Odesanya

引用次数: 0

A Novel Technique for Image Retrieval based on Concatenated Features Extracted from Big Dataset Pre-Trained CNNs 一种基于预训练cnn大数据特征提取的图像检索新技术

International Journal of Image, Graphics and Signal Processing Pub Date : 2023-04-08 DOI: 10.5815/ijigsp.2023.02.01

Chandra Mohan Bhuma, Ramanjaneyulu Kongara

引用次数: 0

Feature Selection based Breast Cancer Prediction 基于特征选择的乳腺癌预测

International Journal of Image, Graphics and Signal Processing Pub Date : 2023-04-08 DOI: 10.5815/ijigsp.2023.02.02

R. Hasan, A. Shafi

引用次数: 0

Polynomial Differentiation Threshold based Edge Detection of Contrast Enhanced Images 基于多项式微分阈值的对比度增强图像边缘检测

International Journal of Image, Graphics and Signal Processing Pub Date : 2023-04-08 DOI: 10.5815/ijigsp.2023.02.04

Kuldip Acharya, D. Ghoshal

引用次数: 0

A Model based on Deep Learning for COVID-19 X-rays Classification 基于深度学习的COVID-19 x射线分类模型

International Journal of Image, Graphics and Signal Processing Pub Date : 2023-02-08 DOI: 10.5815/ijigsp.2023.01.04

Eman I. Abd El-Latif, Nour Eldeen Khalifa

引用次数: 0

Detecting the Third Heart Sound: Separation and Localization from Lung Sound using Intrinsic Time Scale Decomposition 检测第三心音:利用内在时间尺度分解从肺声中分离和定位

International Journal of Image, Graphics and Signal Processing Pub Date : 2023-02-08 DOI: 10.5815/ijigsp.2023.01.03

Sai Bharadwaj B, Ch. Sumanth Kumar

引用次数: 0

Breast Cancer Classification from Ultrasound Images using VGG16 Model based Transfer Learning 基于VGG16模型迁移学习的超声图像乳腺癌分类

International Journal of Image, Graphics and Signal Processing Pub Date : 2023-02-08 DOI: 10.5815/ijigsp.2023.01.02

A. Aowlad Hossain, Jannatul Kamrun Nisha, Fatematuj Johora

{"title":"Breast Cancer Classification from Ultrasound Images using VGG16 Model based Transfer Learning","authors":"A. Aowlad Hossain, Jannatul Kamrun Nisha, Fatematuj Johora","doi":"10.5815/ijigsp.2023.01.02","DOIUrl":"https://doi.org/10.5815/ijigsp.2023.01.02","url":null,"abstract":": Ultrasound based breast screening is gaining attention recently especially for dense breast. The technological advancement, cancer awareness, and cost-safety-availability benefits lead rapid rise of breast ultrasound market. The irregular shape, intensity variation, and additional blood vessels of malignant cancer are distinguishable in ultrasound images from the benign phase. However, classification of breast cancer using ultrasound images is a difficult process owing to speckle noise and complex textures of breast. In this paper, a breast cancer classification method is presented using VGG16 model based transfer learning approach. We have used median filter to despeckle the images. The layers for convolution process of the pretrained VGG16 model along with the maxpooling layers have been used as feature extractor and a proposed fully connected two layers deep neural network has been designed as classifier. Adam optimizer is used with learning rate of 0.001 and binary cross-entropy is chosen as the loss function for model optimization. Dropout of hidden layers is used to avoid overfitting. Breast Ultrasound images from two databases (total 897 images) have been combined to train, validate and test the performance and generalization strength of the classifier. Experimental results showed the training accuracy as 98.2% and testing accuracy as 91% for blind testing data with a reduced of computational complexity. Gradient class activation mapping (Grad-CAM) technique has been used to visualize and check the targeted regions localization effort at the final convolutional layer and found as noteworthy. The outcomes of this work might be useful for the clinical applications of breast cancer diagnosis.","PeriodicalId":378340,"journal":{"name":"International Journal of Image, Graphics and Signal Processing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127433526","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Algorithm of Processing Navigation Information in Systems of Quadrotor Motion Control 四旋翼运动控制系统导航信息处理算法

International Journal of Image, Graphics and Signal Processing Pub Date : 2023-02-08 DOI: 10.5815/ijigsp.2023.01.01

A. Tunik, O. Sushchenko, S. Ilnytska

引用次数: 0