Diagnostic performance of bone scintigraphy analyzed by three artificial neural network systems

2014 ◽  
Vol 29 (2) ◽  
pp. 125-131 ◽  
Author(s):  
Shoichi Kikushima ◽  
Noboru Hanawa ◽  
Fumio Kotake
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Bone Scintigraphy ◽  
Diagnostic Performance ◽  
Network Systems ◽  
Artificial Neural

Logistic Regression and Artificial Neural Network: A Comparative Study in Diagnosing Breast Cancer

2021 ◽  
pp. 6330-6334
Author(s):  
W. Abdul Hameed ◽  
Anuradha D. ◽  
Kaspar S.
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Artificial Neural Network ◽  
Logistic Regression ◽  
Breast Tumor ◽  
Diagnostic Performance ◽  
Malignant Breast Tumor ◽  
Malignant Breast ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Breast tumor is a common problem in gynecology. A reliable test for preoperative discrimination between benign and malignant breast tumor is highly helpful for clinicians in culling the malignant cells through felicitous treatment for patients. This paper is carried out to generate and estimate both logistic regression technique and Artificial Neural Network (ANN) technique to predict the malignancy of breast tumor, utilizing Wisconsin Diagnosis Breast Cancer Database (WDBC). Our aim in this Paper is: (i) to compare the diagnostic performance of both methods in distinguishing between malignant and benign patterns, (ii) to truncate the number of benign cases sent for biopsy utilizing the best model as an auxiliary implement, and (iii) to authenticate the capability of each model to recognize incipient cases as an expert system.

Fully automated analysis for bone scintigraphy with artificial neural network: usefulness of bone scan index (BSI) in breast cancer

2019 ◽  
Vol 33 (10) ◽  
pp. 755-765 ◽  
Author(s):  
Anri Inaki ◽  
Kenichi Nakajima ◽  
Hiroshi Wakabayashi ◽  
Takafumi Mochizuki ◽  
Seigo Kinuya
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Artificial Neural Network ◽  
Bone Scintigraphy ◽  
Bone Scan ◽  
Automated Analysis ◽  
Bone Scan Index ◽  
Artificial Neural

scholarly journals Diagnostic Performance of Artificial Neural Network for Detecting Ischemia in Myocardial Perfusion Imaging

2015 ◽  
Vol 79 (7) ◽  
pp. 1549-1556 ◽  
Author(s):  
Kenichi Nakajima ◽  
Shinro Matsuo ◽  
Hiroshi Wakabayashi ◽  
Kunihiko Yokoyama ◽  
Hisashi Bunko ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Myocardial Perfusion Imaging ◽  
Myocardial Perfusion ◽  
Perfusion Imaging ◽  
Diagnostic Performance ◽  
Artificial Neural

Forming Complicated Surface in Shipyard Using Neural Network System

2012 ◽  
Vol 566 ◽  
pp. 470-475
Author(s):  
Truong Thinh Nguyen
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Induction Heating ◽  
Heating Process ◽  
Ann Model ◽  
Network Systems ◽  
Neural Network System ◽  
Vertical Displacements ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Determining the positions of triangle heating in and parameters of heating process are important for deforming the concave surfaces in shipyard, as well as airplane. The objective of this study was to develop an artificial neural network (ANN) model to predict positions of induction heating and parameters of heating process based on analytical solutions. This model of ANN can help manufacturers determine the positions of induction heating lines and their heating parameters to form a desired shape of plate. The backpropagation neural network systems for determining line-heating positions from object shape of plate are presented in this paper. An artificial neural network model is developed with the relationship between the desired shape of plate and the paths of induction heating. The input data are vertical displacements of plate and the output data are selected heating lines composed by the areas. The outputs of the models were positions of induction heating on plate as well as their parameters. Simulated values obtained with neural network correspond closely to the experimental results.

Diagnostic performance of an artificial neural network to predict excess body fat in children

2018 ◽  
Vol 14 (2) ◽  
Author(s):  
Ibrahim Duran ◽  
Kyriakos Martakis ◽  
Mirko Rehberg ◽  
Oliver Semler ◽  
Eckhard Schoenau
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Body Fat ◽  
Diagnostic Performance ◽  
Artificial Neural

scholarly journals Controlling the Width of Weld Bead from the Weld Pool Using Machine Vision and Artificial Neural Network

2019 ◽  
Vol 269 ◽  
pp. 04003
Author(s):  
Ario Sunar Baskoro ◽  
Duvall Anggraita Purwanto ◽  
Agus Widyianto
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Machine Vision ◽  
Welding Speed ◽  
Vision System ◽  
Welding Current ◽  
Weld Bead ◽  
Network Systems ◽  
Manual Measurement ◽  
Artificial Neural

In this study, the development of artificial neural network systems was proposed to keep the width of weld bead constant by controlling the welding speed. During Gas Tungsten Arc Welding, the weld bead was observed directly using machine vision system that utilized CCD camera. Matlab software was used for image processing algorithm and training the data. In training the data, two methods were used which are training with normalization and without normalization. ANN input parameters were arc current, welding speed, number of pixel and location of weld bead. Double hidden layer was used where each one of them consists of 25 nodes, and the output parameter is new controlled welding speed. The testing data was performed using 100, 105 and 110 A with initial welding speed of 1.35, 1.40 and 1.45 mm/s. The measurement of weld bead was taken using two different methods, machine vision and manual measurement. The result showed that the width of weld bead on welding current of 105 A is close to the target of 7 mm with the average error of 0.49 mm. The best result for the machine vision and manual measurement can be achieved when the welding current is 110 A with a normalization.

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