A novel approach with dual-sampling convolutional neural network for ultrasound image classification of breast tumors

2020 ◽  
Vol 65 (24) ◽  
pp. 245001
Author(s):  
Jiang Xie ◽  
Xiangshuai Song ◽  
Wu Zhang ◽  
Qi Dong ◽  
Yan Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Ultrasound Image ◽  
Breast Tumors ◽  
Novel Approach

Image Classification of Time Series Based on Deep Convolutional Neural Network

2021 ◽  
Author(s):  
Wenjie Cao ◽  
Cheng Zhang ◽  
Zhenzhen Xiong ◽  
Ting Wang ◽  
Junchao Chen ◽  
...  
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Deep Convolutional Neural Network

Multispectral Image Classification of Mural Pigments Based on Convolutional Neural Network

2019 ◽  
Vol 56 (22) ◽  
pp. 221001
Author(s):  
王燕妮 Wang Yanni ◽  
朱丹娜 Zhu Danna ◽  
王慧琴 Wang Huiqin ◽  
王可 Wang Ke
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Multispectral Image

scholarly journals Literature Study of Convolutional Neural Network Algorithm for Batik Classification

2021 ◽  
Vol 1 (1) ◽  
pp. 1-7
Author(s):  
Nardianti Dewi Girsang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Algorithm Analysis ◽  
Image Size ◽  
Literature Study ◽  
Aesthetic Value ◽  
Network Algorithm ◽  
Neural Network Algorithm

Batik is a hereditary cultural heritage that has high aesthetic value and deep philosophy. Currently, Indonesian batik has various types of different motifs and patterns, which are spread in Indonesia with their names and meanings. Batik classification uses Convolutional Neural Network as a pattern recognition method, especially batik image classification. The method used is a literature study, looking at studies from several journals regarding the Convolutional Neural Network Algorithm in Classification and providing conclusions about the usefulness of the algorithm. Analysis This literature study analyzes each journal from previous research related to the Convolutional Neural Network Algorithm in classifying Batik. The results of the analysis, conducted a discussion to better know the characteristics and application of Convolutional Neural Network in the classification of Batik. After discussing, this analysis ends with conclusions about the Convolutional Neural Network algorithm in classifying Batik. Based on previous studies, it can be seen that the convolution neural network can work well for image classification with large datasets. By evaluating the method that has been described by considering the architecture and the level of accuracy, namely getting an accuracy level of 100% with an image size of 128 x 128 and regarding the classification of batik, it shows that image size, image quality, image patterns affect the batik classification process.

Video-Based Human Activity Recognition for Elderly Using Convolutional Neural Network

2020 ◽  
Vol 12 (1) ◽  
pp. 36-48
Author(s):  
Vijayaprabakaran K. ◽  
Sathiyamurthy K. ◽  
Ponniamma M.
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Elderly People ◽  
Activity Recognition ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Human Vision

A typical healthcare application for elderly people involves monitoring daily activities and providing them with assistance. Automatic analysis and classification of an image by the system is difficult compared to human vision. Several challenging problems for activity recognition from the surveillance video involving the complexity of the scene analysis under observations from irregular lighting and low-quality frames. In this article, the authors system use machine learning algorithms to improve the accuracy of activity recognition. Their system presents a convolutional neural network (CNN), a machine learning algorithm being used for image classification. This system aims to recognize and assist human activities for elderly people using input surveillance videos. The RGB image in the dataset used for training purposes which requires more computational power for classification of the image. By using the CNN network for image classification, the authors obtain a 79.94% accuracy in the experimental part which shows their model obtains good accuracy for image classification when compared with other pre-trained models.

scholarly journals Thyroid ultrasound image classification using a convolutional neural network

2021 ◽  
Vol 0 (0) ◽  
pp. 0-0
Author(s):  
Yi-Cheng Zhu ◽  
Peng-Fei Jin ◽  
Jie Bao ◽  
Quan Jiang ◽  
Ximing Wang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Ultrasound Image ◽  
Thyroid Ultrasound

scholarly journals Image classification of malaria using hybrid algorithms: convolutional neural network and method to find appropriate K for K-nearest neighbor

2019 ◽  
Vol 16 (1) ◽  
pp. 382
Author(s):  
Wisit Lumchanow ◽  
Sakol Udomsiri
Keyword(s):  
Neural Network ◽  
Plasmodium Falciparum ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Plasmodium Vivax ◽  
Nearest Neighbor ◽  
Learning Rate ◽  
K Nearest Neighbor ◽  
Ring Form

<span>This paper presents image classification algorithms to improve the learning rate and to comparison the classification efficiency. Using convolutional neural network (CNN) for feature extraction and method to find appropriate k for k-nearest neighbor (KNN). Medical datasets were used in the experiments to classify <span>Plasmodium Vivax and Plasmodium Falciparum. Results of the study indicated that for Plasmodium Vivax in ring form, the appropriate k was 1 and the learning rate (LR) was 83.33%, Trophozoite (k=5, LR=91.67%),</span></span><span> Schizont (k=1, LR=83.33<span>%</span>), and Gametocyte (k=1, LR=<span lang="AR-SA" dir="RTL">91.67</span><span>%</span>) whereas </span><span>Plasmodium Falciparum in ring form</span><span> (k=7, LR=91.67%)<span>,</span> Trophozoite (k=1, LR=83.33%), Schizont (k=1, LR=91.67%) and Gametocyte (k=1, LR=100%).</span>

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