Compression of deep convolutional neural network for computer-aided diagnosis of masses in digital breast tomosynthesis

2018 ◽  
Author(s):  
Ravi K. Samala ◽  
Heang-Ping Chan ◽  
Lubomir M. Hadjiiski ◽  
Mark A. Helvie ◽  
Caleb Richter ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Digital Breast Tomosynthesis ◽  
Computer Aided Diagnosis ◽  
Deep Convolutional Neural Network ◽  
Breast Tomosynthesis ◽  
Computer Aided ◽  
Aided Diagnosis

scholarly journals Comparison Between Feature-Based and Convolutional Neural Network–Based Computer-Aided Diagnosis for Breast Cancer Classification in Digital Breast Tomosynthesis

2019 ◽  
pp. 1-18
Author(s):  
Siwa Chan ◽  
Jinn-Yi Yeh
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Convolutional Neural Network ◽  
Digital Breast Tomosynthesis ◽  
Cancer Classification ◽  
Breast Tomosynthesis ◽  
Breast Cancer Classification ◽  
Computer Aided ◽  
Feature Based ◽  
Aided Diagnosis

Digital breast tomosynthesis (DBT) is a promising new technique for breast cancer diagnosis. DBT has the potential to overcome the tissue superimposition problems that occur on traditional mammograms for tumor detection. However, DBT generates numerous images, thereby creating a heavy workload for radiologists. Therefore, constructing an automatic computer-aided diagnosis (CAD) system for DBT image analysis is necessary. This study compared feature-based CAD and convolutional neural network (CNN)-based CAD for breast cancer classification from DBT images. The research methods included image preprocessing, candidate tumor identification, three-dimensional feature generation, classification, image cropping, augmentation, CNN model design, and deep learning. The precision rates (standard deviation) of the LeNet-based CNN CAD and the feature-based CAD for breast cancer classification were 89.84 (0.013) and 84.46 (0.082), respectively. The T value was -4.091 and the P value was 0.00 < 0.05, which indicate that the LeNet-based CNN CAD significantly outperform the feature-based CAD. However, there is no significantly differences between the LeNet-based CNN CAD and the feature-based CAD on other criteria. The results can be applied to clinical medicine and assist radiologists in breast cancer identification.

scholarly journals Computer-Aided Diagnosis Research of a Lung Tumor Based on a Deep Convolutional Neural Network and Global Features

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Huiling Lu
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Gradient Descent ◽  
Lung Tumor ◽  
Recognition Rate ◽  
Computer Aided Diagnosis ◽  
Deep Convolutional Neural Network ◽  
Model Parameters ◽  
Computer Aided ◽  
Aided Diagnosis

Based on the better generalization ability and the feature learning ability of the deep convolutional neural network, it is very significant to use the DCNN on the computer-aided diagnosis of a lung tumor. Firstly, a deep convolutional neural network was constructed according to the fuzzy characteristics and the complexity of lung CT images. Secondly, the relation between model parameters (iterations, different resolution) and recognition rate is discussed. Thirdly, the effects of different model structures for the identification of a lung tumor were analyzed by changing convolution kernel size, feature dimension, and depth of the network. Fourthly, the different optimization methods on how to influence the DCNN performance were discussed from three aspects containing pooling methods (maximum pooling and mean pooling), activation function (sigmoid and ReLU), and training algorithm (batch gradient descent and gradient descent with momentum). Finally, the experimental results verified the feasibility of DCNN used on computer-aided diagnosis of lung tumors, and it can achieve a good recognition rate when selecting the appropriate model parameters and model structure and using the method of gradient descent with momentum.

Computer-aided diagnosis for burnt skin images using deep convolutional neural network

2020 ◽  
Vol 79 (45-46) ◽  
pp. 34545-34568 ◽  
Author(s):  
Fakhri Alam Khan ◽  
Ateeq Ur Rehman Butt ◽  
Muhammad Asif ◽  
Waqar Ahmad ◽  
Muhammad Nawaz ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Computer Aided Diagnosis ◽  
Deep Convolutional Neural Network ◽  
Computer Aided ◽  
Aided Diagnosis

Research on Computer-Aided Diagnosis of Alzheimer’s Disease Based on Heterogeneous Medical Data Fusion

2019 ◽  
Vol 33 (05) ◽  
pp. 1957001
Author(s):  
Yin Dai ◽  
Daoyun Qiu ◽  
Yang Wang ◽  
Sizhe Dong ◽  
Hong-Li Wang
Keyword(s):  
Neural Network ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Convolutional Neural Network ◽  
Effective Means ◽  
Computer Aided Diagnosis ◽  
Test Results ◽  
Back Propagation Algorithm ◽  
Computer Aided ◽  
Aided Diagnosis

Alzheimer’s disease is the third most expensive disease, only after cancer and cardiopathy. It is also the fourth leading cause of death in the elderly after cardiopathy, cancer, and cerebral palsy. The disease lacks specific diagnostic criteria. At present, there is still no definitive and effective means for preclinical diagnosis and treatment. It is the only disease that cannot be prevented and cured among the world’s top ten fatal diseases. It has now been proposed as a global issue. Computer-aided diagnosis of Alzheimer’s disease (AD) is mostly based on images at this stage. This project uses multi-modality imaging MRI/PET combining with clinical scales and uses deep learning-based computer-aided diagnosis to treat AD, improves the comprehensiveness and accuracy of diagnosis. The project uses Bayesian model and convolutional neural network to train experimental data. The experiment uses the improved existing network model, LeNet-5, to design and build a 10-layer convolutional neural network. The network uses a back-propagation algorithm based on a gradient descent strategy to achieve good diagnostic results. Through the calculation of sensitivity, specificity and accuracy, the test results were evaluated, good test results were obtained.

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