Deep Learning Based Capsule Neural Network Model for Breast Cancer Diagnosis Using Mammogram Images

2021 ◽  
Author(s):  
T. Kavitha ◽  
Paul P. Mathai ◽  
C. Karthikeyan ◽  
M. Ashok ◽  
Rachna Kohar ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Deep Learning ◽  
Network Model ◽  
Cancer Diagnosis ◽  
Neural Network Model ◽  
Breast Cancer Diagnosis ◽  
Mammogram Images

scholarly journals Machine Learning-Based Gynecologic Tumor Diagnosis and Its Postoperative Incisional Infection Influence Factor Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Qian Shen ◽  
Ling Wang
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Cancer Diagnosis ◽  
Neural Network Model ◽  
Breast Cancer Diagnosis ◽  
The Self ◽  
Adaptive Neural Network ◽  
Tumor Patients ◽  
Gynecologic Tumor ◽  
Gynecologic Tumors

Various factors influencing postoperative incisional infection in gynecologic tumors were analyzed, and the value of quality nursing intervention was studied. In this study, 74 surgically treated gynecologic tumor patients were randomly selected from within the hospital as the study population and were divided into study and control groups. For this purpose, the whole-group random sampling method is utilized to compare the postoperative incisional infection rates of the two groups, analyze their influencing factors, and develop quality nursing interventions. In this paper, a breast cancer diagnosis prediction model was developed by combining the self-attentive mechanism. The preprocessing work such as data quantification and normalization was performed first which is followed by adding the preprocessed data to the self-attentive mechanism. This model has solved the problem that recurrent neural networks (RNNs) could not extract and calculate the features at the same time. Likewise, it has solved the drawback that the RNN could not consider global features at the same time when extracting the features, and then, the feature matrix extracted by the self-attentive mechanism was added to the adaptive neural network. The adaptive neural network model for breast cancer diagnosis prediction was constructed and, finally, relevant parameters of the adaptive neural network model were adjusted according to different tasks to make the model performance optimal. Experimental results showed that the postoperative incision infection rate of patients in the study group was 2.70%, which was significantly lower than that of 21.62% in the control group ( P < 0.05 ). Likewise, operation time, operation method, hospitalization time, preoperative fever, diabetes mellitus, and anemia were the main influencing factors of postoperative incision infection in women with gynecologic tumors. The time of surgery, surgical method, long hospital stay, preoperative fever, diabetes, and anemia are the main factors that lead to postoperative incisional infection in female gynecologic tumor patients.

scholarly journals RISC-V Virtual Platform-Based Convolutional Neural Network Accelerator Implemented in SystemC

Electronics ◽  
2021 ◽  
Vol 10 (13) ◽  
pp. 1514
Author(s):  
Seung-Ho Lim ◽  
WoonSik William Suh ◽  
Jin-Young Kim ◽  
Sang-Young Cho
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Network Model ◽  
Neural Network Model ◽  
Deep Neural Network ◽  
System Level ◽  
Neural Network Models ◽  
Data Set ◽  
Embedded Device ◽  
Virtual Platform

The optimization for hardware processor and system for performing deep learning operations such as Convolutional Neural Networks (CNN) in resource limited embedded devices are recent active research area. In order to perform an optimized deep neural network model using the limited computational unit and memory of an embedded device, it is necessary to quickly apply various configurations of hardware modules to various deep neural network models and find the optimal combination. The Electronic System Level (ESL) Simulator based on SystemC is very useful for rapid hardware modeling and verification. In this paper, we designed and implemented a Deep Learning Accelerator (DLA) that performs Deep Neural Network (DNN) operation based on the RISC-V Virtual Platform implemented in SystemC in order to enable rapid and diverse analysis of deep learning operations in an embedded device based on the RISC-V processor, which is a recently emerging embedded processor. The developed RISC-V based DLA prototype can analyze the hardware requirements according to the CNN data set through the configuration of the CNN DLA architecture, and it is possible to run RISC-V compiled software on the platform, can perform a real neural network model like Darknet. We performed the Darknet CNN model on the developed DLA prototype, and confirmed that computational overhead and inference errors can be analyzed with the DLA prototype developed by analyzing the DLA architecture for various data sets.

Deep learning model for protein disease classification

2021 ◽  
Vol 16 ◽  
Author(s):  
Farida Alaaeldin Mostafa ◽  
Yasmine Mohamed Afify ◽  
Rasha Mohamed Ismail ◽  
Nagwa Lotfy Badr
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Protein Analysis ◽  
Kernel Functions ◽  
Support Vector ◽  
Feature Descriptors

Background: Protein sequence analysis helps in the prediction of protein functions. As the number of proteins increases, it gives the bioinformaticians a challenge to analyze and study the similarity between them. Most of the existing protein analysis methods use Support Vector Machine. Deep learning did not receive much attention regarding protein analysis as it is noted that little work focused on studying the protein diseases classification. Objective: The contribution of this paper is to present a deep learning approach that classifies protein diseases based on protein descriptors. Methods: Different protein descriptors are used and decomposed into modified feature descriptors. Uniquely, we introduce using Convolutional Neural Network model to learn and classify protein diseases. The modified feature descriptors are fed to the Convolutional Neural Network model on a dataset of 1563 protein sequences classified into 3 different disease classes: Aids, Tumor suppressor, and Proto oncogene. Results: The usage of the modified feature descriptors shows a significant increase in the performance of the Convolutional Neural Network model over Support Vector Machine using different kernel functions. One modified feature descriptor improved by 19.8%, 27.9%, 17.6%, 21.5%, 17.3%, and 22% for evaluation metrics: Area Under the Curve, Matthews Correlation Coefficient, Accuracy, F1-score, Recall, and Precision, respectively. Conclusion: Results show that the prediction of the proposed modified feature descriptors significantly surpasses that of Support Vector Machine model.

scholarly journals Nowcasting of Lumber Futures Price with Google Trends Index Using Machine Learning and Deep Learning Models

2021 ◽  
Vol 72 (1) ◽  
pp. 11-20
Author(s):  
Mingtao He ◽  
Wenying Li ◽  
Brian K. Via ◽  
Yaoqi Zhang
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Network Model ◽  
Neural Network Model ◽  
Price Volatility ◽  
Google Trends ◽  
Learning Models ◽  
Fresh Perspective ◽  
Futures Price

Abstract Firms engaged in producing, processing, marketing, or using lumber and lumber products always invest in futures markets to reduce the risk of lumber price volatility. The accurate prediction of real-time prices can help companies and investors hedge risks and make correct market decisions. This paper explores whether Internet browsing habits can accurately nowcast the lumber futures price. The predictors are Google Trends index data related to lumber prices. This study offers a fresh perspective on nowcasting the lumber price accurately. The novel outlook of employing both machine learning and deep learning methods shows that despite the high predictive power of both the methods, on average, deep learning models can better capture trends and provide more accurate predictions than machine learning models. The artificial neural network model is the most competitive, followed by the recurrent neural network model.

scholarly journals Image Classification Using Transfer Learning and Deep Learning

2021 ◽  
Vol 10 (9) ◽  
pp. 25394-25398
Author(s):  
Chitra Desai
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Image Classification ◽  
Transfer Learning ◽  
Network Model ◽  
Neural Network Model ◽  
Large Scale ◽  
Deep Neural Network ◽  
Visual Recognition ◽  
Classification Of Images

Deep learning models have demonstrated improved efficacy in image classification since the ImageNet Large Scale Visual Recognition Challenge started since 2010. Classification of images has further augmented in the field of computer vision with the dawn of transfer learning. To train a model on huge dataset demands huge computational resources and add a lot of cost to learning. Transfer learning allows to reduce on cost of learning and also help avoid reinventing the wheel. There are several pretrained models like VGG16, VGG19, ResNet50, Inceptionv3, EfficientNet etc which are widely used.   This paper demonstrates image classification using pretrained deep neural network model VGG16 which is trained on images from ImageNet dataset. After obtaining the convolutional base model, a new deep neural network model is built on top of it for image classification based on fully connected network. This classifier will use features extracted from the convolutional base model.

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