Computer-aided diagnosis with a convolutional neural network algorithm for fully automated detection of prostate cancer using pre-biopsy MRI

2018 ◽  
Vol 17 (2) ◽  
pp. e305-e306
Author(s):  
J. Ishioka ◽  
Y. Matsuoka ◽  
S. Uehara ◽  
Y. Yasuda ◽  
T. Kijima ◽  
...  
Keyword(s):  
Neural Network ◽  
Prostate Cancer ◽  
Convolutional Neural Network ◽  
Computer Aided Diagnosis ◽  
Automated Detection ◽  
Network Algorithm ◽  
Computer Aided ◽  
Neural Network Algorithm ◽  
Aided Diagnosis

Computer-aided diagnosis of prostate cancer on magnetic resonance imaging using a convolutional neural network algorithm

2018 ◽  
Vol 122 (3) ◽  
pp. 411-417 ◽  
Author(s):  
Junichiro Ishioka ◽  
Yoh Matsuoka ◽  
Sho Uehara ◽  
Yosuke Yasuda ◽  
Toshiki Kijima ◽  
...  
Keyword(s):  
Neural Network ◽  
Magnetic Resonance Imaging ◽  
Prostate Cancer ◽  
Magnetic Resonance ◽  
Convolutional Neural Network ◽  
Computer Aided Diagnosis ◽  
Resonance Imaging ◽  
Computer Aided ◽  
Neural Network Algorithm ◽  
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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