A computer assisted diagnosis system for malignant melanoma using 3D skin surface texture features and artificial neural network

2010 ◽  
Vol 9 (4) ◽  
pp. 370 ◽  
Author(s):  
Yi Ding ◽  
Lyndon Smith ◽  
Melvyn Smith ◽  
Jiuai Sun ◽  
Robert Warr
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Malignant Melanoma ◽  
Surface Texture ◽  
Texture Features ◽  
Skin Surface ◽  
Computer Assisted ◽  
Diagnosis System ◽  
Assisted Diagnosis ◽  
3D Skin

Combination of 3D skin surface texture features and 2D ABCD features for improved melanoma diagnosis

2015 ◽  
Vol 53 (10) ◽  
pp. 961-974 ◽  
Author(s):  
Yi Ding ◽  
Nigel W. John ◽  
Lyndon Smith ◽  
Jiuai Sun ◽  
Melvyn Smith
Keyword(s):  
Surface Texture ◽  
Texture Features ◽  
Skin Surface ◽  
Melanoma Diagnosis ◽  
3D Skin

scholarly journals Computer-Assisted Diagnosis System for Breast Cancer in Computed Tomography Laser Mammography (CTLM)

2017 ◽  
Vol 30 (6) ◽  
pp. 796-811 ◽  
Author(s):  
Afsaneh Jalalian ◽  
Syamsiah Mashohor ◽  
Rozi Mahmud ◽  
Babak Karasfi ◽  
M. Iqbal Saripan ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Computed Tomography ◽  
Computer Assisted ◽  
Diagnosis System ◽  
Computer Assisted Diagnosis ◽  
Assisted Diagnosis

Cardiac Arrhythmia Detection using Dual Tree Wavelet Transform and Convolutional Neural Network

2021 ◽  
Author(s):  
K Reddy Madhavi ◽  
Padmavathi kora ◽  
L Venkateswara Reddy ◽  
J Avanija ◽  
KLS Soujanya ◽  
...  
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Cardiac Cells ◽  
P Wave ◽  
Computer Assisted ◽  
Ecg Signals ◽  
Coronary Diseases ◽  
Life Saving ◽  
Life Threatening ◽  
Assisted Diagnosis

Abstract The non-stationary ECG signals are used as a key tools in screening coronary diseases. ECG recording is collected from millions of cardiac cells’ and depolarization and re-polarization conducted in a synchronized manner as: The P-wave occurs first, followed by the QRScomplex and the T-wave, which will repeat in each beat. The signal is altered in a cardiac beat period for different heart conditions. This change can be observed in order to diagnose the patient’s heart status. There are life-threatening (critical) and non-life - threatening (noncritical) arrhythmia (abnormal Heart). Critical arrhythmia gives little time for surgery, whereas non-critical needs additional life-saving care. Simple naked eye diagnosis can mislead the detection. At that point, Computer Assisted Diagnosis (CAD) is therefore required. In this paper Dual Tree Wavelet Transform (DTWT) used as a feature extraction technique along with Convolution Neural Network (CNN) to detect abnormal Heart. The findings of this research and associated studies are without any cumbersome artificial environments. The CAD method proposed has high generalizability; it can help doctors efficiently identify diseases and decrease misdiagnosis.

Forecasting of Cutting Forces in Dental Adjustment of Ceramic Prostheses Using an Artificial Neural Network

2010 ◽  
Vol 152-153 ◽  
pp. 1687-1690
Author(s):  
Jian Hui Peng ◽  
Xiao Fei Song ◽  
Ling Yin
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Cutting Forces ◽  
High Speed ◽  
Cutting Process ◽  
Computer Assisted ◽  
Ann Model ◽  
Operational Parameters ◽  
Artificial Neural

Intraoral adjustment of ceramic prostheses involving cutting process is a central procedure in restorative dentistry because the quality of ceramic prostheses depends on the cutting process. In this paper, an artificial neural network (ANN) model was developed for the first time to forecast the dynamic forces in dental cutting process as functions of clinical operational parameters. The predicted force values were compared with the measured values in in vitro dental cutting of porcelain prostheses obtained using a novel two-degrees-of-freedom computer-assisted testing apparatus with a high-speed dental handpiece and diamond burs. The results indicate that there existed nonlinear relationships between the cutting forces and clinical operational parameters. It is found that the ANN-forecasted forces were in good agreement with the experiment-measured values. This indicates that the established ANN model can provide insights into the force-related process assessment and forecast for clinical dental cutting of ceramic prostheses.

Applying Artificial Neural Network Model on Investigating the Fiber Diameter of Polybutylene Terephthalate (PBT) Spunbonding Nonwovens: Comparison with Mathematical Statistical Method

2010 ◽  
Vol 426-427 ◽  
pp. 692-696
Author(s):  
Bo Zhao
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Statistical Method ◽  
Network Model ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
Fiber Diameter ◽  
Computer Assisted ◽  
Polybutylene Terephthalate ◽  
Artificial Neural

In this paper, two models are founded and introduced to predict the fiber diameter of polybutylene terephthalate spunbonding nonwovens from the spunbonding process parameters. The results indicate the artificial neural network model has good approximation capability and fast convergence rate, and it can provide quantitative predictions of fiber diameter and yield more accurate and stable predictions than the mathematical statistical method. This area of research has great potential in the field of computer assisted design in spunbonding technology.

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