Computer-aided diagnosis of prostate cancer using a deep convolutional neural network from multiparametric MRI

2018 ◽  
Vol 48 (6) ◽  
pp. 1570-1577 ◽  
Author(s):  
Yang Song ◽  
Yu-Dong Zhang ◽  
Xu Yan ◽  
Hui Liu ◽  
Minxiong Zhou ◽  
...  
Keyword(s):  
Neural Network ◽  
Prostate Cancer ◽  
Convolutional Neural Network ◽  
Computer Aided Diagnosis ◽  
Multiparametric Mri ◽  
Deep Convolutional Neural Network ◽  
Computer Aided ◽  
Aided Diagnosis

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

scholarly journals Computer-Aided Diagnosis Improves the Detection of Clinically Significant Prostate Cancer on Multiparametric-MRI: A Multi-Observer Performance Study Involving Inexperienced Readers

Diagnostics ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 973
Author(s):  
Valentina Giannini ◽  
Simone Mazzetti ◽  
Giovanni Cappello ◽  
Valeria Maria Doronzio ◽  
Lorenzo Vassallo ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Computer Aided Diagnosis ◽  
Multiparametric Mri ◽  
Performance Study ◽  
Predictive Values ◽  
Cad System ◽  
Computer Aided ◽  
Clinically Significant ◽  
Sensitivity Specificity ◽  
Aided Diagnosis

Recently, Computer Aided Diagnosis (CAD) systems have been proposed to help radiologists in detecting and characterizing Prostate Cancer (PCa). However, few studies evaluated the performances of these systems in a clinical setting, especially when used by non-experienced readers. The main aim of this study is to assess the diagnostic performance of non-experienced readers when reporting assisted by the likelihood map generated by a CAD system, and to compare the results with the unassisted interpretation. Three resident radiologists were asked to review multiparametric-MRI of patients with and without PCa, both unassisted and assisted by a CAD system. In both reading sessions, residents recorded all positive cases, and sensitivity, specificity, negative and positive predictive values were computed and compared. The dataset comprised 90 patients (45 with at least one clinically significant biopsy-confirmed PCa). Sensitivity significantly increased in the CAD assisted mode for patients with at least one clinically significant lesion (GS > 6) (68.7% vs. 78.1%, p = 0.018). Overall specificity was not statistically different between unassisted and assisted sessions (94.8% vs. 89.6, p = 0.072). The use of the CAD system significantly increases the per-patient sensitivity of inexperienced readers in the detection of clinically significant PCa, without negatively affecting specificity, while significantly reducing overall reporting time.

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