Convolutional neural network-based decision support system for bladder cancer staging in CT urography: decision threshold estimation and validation

2020 ◽  
Author(s):  
Daniel H. Chapman-Sung ◽  
Lubomir Hadjiiski ◽  
Dhanuj Gandikota ◽  
Heang-Ping Chan ◽  
Ravi Samala ◽  
...  
Keyword(s):  
Neural Network ◽  
Bladder Cancer ◽  
Decision Support ◽  
Convolutional Neural Network ◽  
Support System ◽  
Cancer Staging ◽  
Ct Urography ◽  
Decision Threshold ◽  
Threshold Estimation ◽  
Bladder Cancer Staging

scholarly journals Urinary bladder cancer staging in CT urography using machine learning

2017 ◽  
Vol 44 (11) ◽  
pp. 5814-5823 ◽  
Author(s):  
Sankeerth S. Garapati ◽  
Lubomir Hadjiiski ◽  
Kenny H. Cha ◽  
Heang-Ping Chan ◽  
Elaine M. Caoili ◽  
...  
Keyword(s):  
Machine Learning ◽  
Bladder Cancer ◽  
Urinary Bladder ◽  
Urinary Bladder Cancer ◽  
Cancer Staging ◽  
Ct Urography ◽  
Bladder Cancer Staging

A multiparametric MRI-based CAD system for accurate diagnosis of bladder cancer staging

2021 ◽  
Vol 90 ◽  
pp. 101911
Author(s):  
K. Hammouda ◽  
F. Khalifa ◽  
A. Soliman ◽  
M. Ghazal ◽  
M. Abou El-Ghar ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Cancer Staging ◽  
Multiparametric Mri ◽  
Accurate Diagnosis ◽  
Cad System ◽  
Bladder Cancer Staging

scholarly journals Clinical research Technique of transurethral needle core biopsy to confirm invasive bladder cancer staging

2010 ◽  
Vol 3 ◽  
pp. 388-392
Author(s):  
Piotr Chłosta ◽  
Artur A. Antoniewicz ◽  
Tomasz Szopiński ◽  
Jakub Dobruch ◽  
Andrzej Borówka
Keyword(s):  
Bladder Cancer ◽  
Clinical Research ◽  
Core Biopsy ◽  
Cancer Staging ◽  
Invasive Bladder Cancer ◽  
Needle Core Biopsy ◽  
Research Technique ◽  
Bladder Cancer Staging

scholarly journals Weighted Deep Neural Network Based Clinical Decision Support System for the Determination of Fetal Health

2019 ◽  
Vol 8 (4) ◽  
pp. 8564-8569
Keyword(s):  
Neural Network ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Deep Neural Network ◽  
Predictive Analytics ◽  
Clinical Decision ◽  
Svm Classifier ◽  
Healthcare Data ◽  
The Individual

Healthcare industry is undergoing changes at a tremendous rate due to healthcare innovations. Predictive analytics is increasingly being used to diagnose the patient’s ailments and provide actionable insights into already existing healthcare data. The paper looks at a decision support system for determining the health status of the foetus from cardiotographic data using deep learning neural networks. The foetal health records are classified as normal, suspect and pathological. As the multiclass cardiotographic datset of the foetus shows a high degree of imbalance a weighted deep neural network is applied. To overcome the accuracy paradox due to the multiclass imbalance, relevant metrics such as the sensitivity, specificity, F1 Score and Gmean are used to measure the performance of the classifier rather than accuracy. The metrics are applied to the individual classes to ensure that the positive cases are identified correctly. The weighted DNN based classifier is able to classify the positive instances with Gmean score of 91% which is better than than the SVM classifier.

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