Modelling and optimisation of treatment parameters in high-dose-rate mono brachytherapy for localised prostate carcinoma using a multilayer artificial neural network and a genetic algorithm: Pilot study

2020 ◽  
Vol 126 ◽  
pp. 104045
Author(s):  
Katarina M. Rajković ◽  
Kata Dabić-Stanković ◽  
Jovan Stanković ◽  
Miodrag Aćimović ◽  
Nina Đukanović ◽  
...  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Pilot Study ◽  
Dose Rate ◽  
Prostate Carcinoma ◽  
High Dose Rate ◽  
High Dose ◽  
Artificial Neural

scholarly journals The Use of the Artificial Neural Network for the Treatment Outcomes of Single-channel and Tri-channel Applicator Used in Cervical Cancer Based on High Dose Rate Brachytherapy

2021 ◽  
Author(s):  
Alamgir Hossain ◽  
Shahidul Miah ◽  
Prodip Kumer Ray ◽  
Ashim Kumar Ghosh ◽  
Rawshan Ara Khatun ◽  
...  
Keyword(s):  
Neural Network ◽  
Cervical Cancer ◽  
Artificial Neural Network ◽  
Dose Rate ◽  
Treatment Outcomes ◽  
Single Channel ◽  
High Dose Rate ◽  
High Dose ◽  
High Dose Rate Brachytherapy ◽  
Artificial Neural

Abstract The study aimed to evaluate the treatment outcomes of single-channel and tri-channel applicators for cervical cancer patients based on high dose rate brachytherapy using an artificial neural network. An artificial neural network (ANNs) model is proposed to predict the treatment outcomes for the single-channel applicator and tri-channel applicator in cervical cancer for high dose rate brachytherapy. Fifty-four patients of cervical cancer who were receiving external beam radiation therapy (EBRT) of 40-50 cGy, with chemotherapy, were selected in this study from 37 patients with cervical cancer being used to train and 17 for testing in this model. A model was developed for intracavitary brachytherapy to estimate the comparison of　treatment outcomes for the single-channel applicator and tri-channel applicators, demonstrating the sensitivity 100% and specificity 100 % and accuracy 100% for training and 87.5%, 77.8%, and 82.4% for testing, respectively, including AUC= 0.961. The survival rate was 85% and 95% for single-channel and tri-channel applicators at 2 years, respectively. A model approach for artificial neural networks based on gynecological brachytherapy is a promising method for patient's treatment, resulting in the dosimetry output of applicators; medical physicists can be decided the appropriate applicator for cervical cancer. The proposed model has the potential accuracy in judging the treatment outcomes for the single-channel applicator and tri-channel applicator in cervical cancer based on survival analysis.

scholarly journals Modeling and Optimizing a New Culture Medium for In Vitro Rooting of G×N15 Prunus Rootstock using Artificial Neural Network-Genetic Algorithm

2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Mohammad Mehdi Arab ◽  
Abbas Yadollahi ◽  
Maliheh Eftekhari ◽  
Hamed Ahmadi ◽  
Mohammad Akbari ◽  
...  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Culture Medium ◽  
In Vitro Rooting ◽  
Artificial Neural

Development of a Hybrid Artificial Neural Network and Genetic Algorithm Model for Regime Identification of Slurry Transport in Pipelines

2009 ◽  
Vol 4 (1) ◽  
Author(s):  
Sandip K Lahiri ◽  
Kartik Chandra Ghanta
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Operating Conditions ◽  
Misclassification Error ◽  
Wide Range ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Pipeline Design ◽  
Hybrid Artificial Neural Network

Four distinct regimes were found existent (namely sliding bed, saltation, heterogeneous suspension and homogeneous suspension) in slurry flow in pipeline depending upon the average velocity of flow. In the literature, few numbers of correlations has been proposed for identification of these regimes in slurry pipelines. Regime identification is important for slurry pipeline design as they are the prerequisite to apply different pressure drop correlation in different regime. However, available correlations fail to predict the regime over a wide range of conditions. Based on a databank of around 800 measurements collected from the open literature, a method has been proposed to identify the regime using artificial neural network (ANN) modeling. The method incorporates hybrid artificial neural network and genetic algorithm technique (ANN-GA) for efficient tuning of ANN meta parameters. Statistical analysis showed that the proposed method has an average misclassification error of 0.03%. A comparison with selected correlations in the literature showed that the developed ANN-GA method noticeably improved prediction of regime over a wide range of operating conditions, physical properties, and pipe diameters.

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