scholarly journals Texture analysis ofT1- andT2-weighted MR images and use of probabilistic neural network to discriminate posterior fossa tumours in children

2014 ◽  
Vol 27 (6) ◽  
pp. 632-639 ◽  
Author(s):  
Eleni Orphanidou-Vlachou ◽  
Nikolaos Vlachos ◽  
Nigel P. Davies ◽  
Theodoros N. Arvanitis ◽  
Richard G. Grundy ◽  
...  
Keyword(s):  
Neural Network ◽  
Texture Analysis ◽  
Posterior Fossa ◽  
Probabilistic Neural Network ◽  
Mr Images ◽  
Posterior Fossa Tumours

scholarly journals Improved prediction of postoperative paediatric cerebellar mutism syndrome using an artificial neural network

2022 ◽  
Author(s):  
Jai Sidpra ◽  
Adam P Marcus ◽  
Ulrike Löbel ◽  
Sebastian M Toescu ◽  
Derek Yecies ◽  
...  
Keyword(s):  
Neural Network ◽  
Risk Factors ◽  
Artificial Neural Network ◽  
Logistic Regression ◽  
Posterior Fossa ◽  
Area Under The Curve ◽  
Rank Test ◽  
Cerebellar Mutism ◽  
Posterior Fossa Tumours ◽  
Artificial Neural

Abstract Background Postoperative paediatric cerebellar mutism syndrome (pCMS) is a common but severe complication which may arise following the resection of posterior fossa tumours in children. Two previous studies have aimed to preoperatively predict pCMS, with varying results. In this work, we examine the generalisation of these models and determine if pCMS can be predicted more accurately using an artificial neural network (ANN). Methods An overview of reviews was performed to identify risk factors for pCMS, and a retrospective dataset collected as per these defined risk factors from children undergoing resection of primary posterior fossa tumours. The ANN was trained on this dataset and its performance evaluated in comparison to logistic regression and other predictive indices via analysis of receiver operator characteristic curves. Area under the curve (AUC) and accuracy were calculated and compared using a Wilcoxon signed rank test, with p<0.05 considered statistically significant. Results 204 children were included, of whom 80 developed pCMS. The performance of the ANN (AUC 0.949; accuracy 90.9%) exceeded that of logistic regression (p<0.05) and both external models (p<0.001). Conclusion Using an ANN, we show improved prediction of pCMS in comparison to previous models and conventional methods.

Alzheimer disease detection from structural MR images using FCM based weighted probabilistic neural network

2018 ◽  
Vol 13 (1) ◽  
pp. 87-110 ◽  
Author(s):  
Baskar Duraisamy ◽  
Jayanthi Venkatraman Shanmugam ◽  
Jayanthi Annamalai
Keyword(s):  
Neural Network ◽  
Alzheimer Disease ◽  
Probabilistic Neural Network ◽  
Disease Detection ◽  
Mr Images

scholarly journals An Efficient Method for Automatic Classification of Brain MRI using Feature Selection and Modified Probabilistic Neural Network

2019 ◽  
Vol 9 (2) ◽  
pp. 976-981
Keyword(s):  
Neural Network ◽  
Feature Selection ◽  
Probabilistic Neural Network ◽  
Brain Mri ◽  
Mr Images ◽  
Tumor Identification ◽  
Texture Characteristics ◽  
Minimization Technique ◽  
Occurrence Matrix

In the brain tumor MRI images, the identification, segmentation and detection of the infectious area is a tedious and lengthy task. As segmentation is called intensity inhomogeneity by an intrinsic object. In this paper we suggest an energy efficient minimization technique for joint domain assessment and segmentation of MR images called multiplicative intrinsic component optimization (MICO). In this work, we focused on quicker implementation with a robust removal of gray-level co-occurrence matrix (GLCM). Optimal texture characteristics are obtained by the Spatial Gray Dependence (SGLDM) technique from ordinary and tumor areas. With very large feature sets, the choice of features is redundant because the precision frequently worsens without choice of features. However, when only the feature selection is used, the precision of classification is significantly improved. However, by reducing the time needed for classification computations and improving classification precision by removing redundant, false or incorrect characteristics. A fresh function choice and weighting technique, supported by the decomposition developmental multi-objective algorithm, are provided in this work. These characteristics are provided for the MPNN classification. Modified probabilistic neural network (MPNN) classification was used in brain MRI images for training and testing for precision in tumor identification. The simulation findings accomplished almost 98% precision in the identification of ordinary and abnormal tissue from brain MR images showing the efficiency of the method suggested.

Classification and Identification of Power System Disturbances Using Wavalet and Artificial Neural Network Technique

2005 ◽  
Vol 2 (2) ◽  
pp. 25
Author(s):  
Noraliza Hamzah ◽  
Wan Nor Ainin Wan Abdullah ◽  
Pauziah Mohd Arsad
Keyword(s):  
Neural Network ◽  
Power Quality ◽  
Probabilistic Neural Network ◽  
Microsoft Excel ◽  
Waveform Data ◽  
The Neural Network ◽  
Harmonic Voltage ◽  
Power Quality Disturbances ◽  
Electronic Load ◽  
Wavelet Technique

Power Quality disturbances problems have gained widespread interest worldwide due to the proliferation of power electronic load such as adjustable speed drives, computer, industrial drives, communication and medical equipments. This paper presents a technique based on wavelet and probabilistic neural network to detect and classify power quality disturbances, which are harmonic, voltage sag, swell and oscillatory transient. The power quality disturbances are obtained from the waveform data collected from premises, which include the UiTM Sarawak, Faculty of Science Computer in Shah Alam, Jati College, Menara UiTM, PP Seksyen 18 and Putra LRT. Reliable Power Meter is used for data monitoring and the data is further processed using the Microsoft Excel software. From the processed data, power quality disturbances are detected using the wavelet technique. After the disturbances being detected, it is then classified using the Probabilistic Neural Network. Sixty data has been chosen for the training of the Probabilistic Neural Network and ten data has been used for the testing of the neural network. The results are further interfaced using matlab script code.  Results from the research have been very promising which proved that the wavelet technique and Probabilistic Neural Network is capable to be used for power quality disturbances detection and classification.

Sign in / Sign up

Export Citation Format

Share Document