Detection of bone fractures using image processing techniques and artificial neural networks

2017 ◽  
Author(s):  
Ozgur Ozturk ◽  
Hakan Kutucu
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Artificial Neural Networks ◽  
Bone Fractures ◽  
Image Processing Techniques ◽  
Artificial Neural ◽  
Processing Techniques

Neural Networks in Medicine

2011 ◽  
pp. 744-765
Author(s):  
Rajasvaran Logeswaran
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Artificial Neural Networks ◽  
Bile Ducts ◽  
Magnetic Resonance Cholangiopancreatography ◽  
Automated System ◽  
Clinical Test ◽  
Multi Layer Perceptron ◽  
Image Processing Techniques ◽  
Processing Techniques

Automatic detection of tumors in the bile ducts of the liver is very difficult as often, in the defacto noninvasive diagnostic images using magnetic resonance cholangiopancreatography (MRCP), tumors are not clearly visible. Specialists use their experience in anatomy to diagnose a tumor by absence of expected structures in the images. Naturally, undertaking such diagnosis is very difficult for an automated system. This chapter proposes an algorithm that is based on a combination of the manual diagnosis principles along with nature-inspired image processing techniques and artificial neural networks (ANN) to assist in the preliminary diagnosis of tumors affecting the bile ducts in the liver. The results obtained show over 88% success rate of the system developed using an ANN with the multi-layer perceptron (MLP) architecture, in performing the difficult automated preliminary detection of the tumors, even in the robust clinical test images with other biliary diseases present.

Neural Networks in Medicine

2010 ◽  
pp. 144-165
Author(s):  
Rajasvaran Logeswaran
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Artificial Neural Networks ◽  
Bile Ducts ◽  
Magnetic Resonance Cholangiopancreatography ◽  
Automated System ◽  
Clinical Test ◽  
Multi Layer Perceptron ◽  
Image Processing Techniques ◽  
Processing Techniques

Automatic detection of tumors in the bile ducts of the liver is very difficult as often, in the defacto noninvasive diagnostic images using magnetic resonance cholangiopancreatography (MRCP), tumors are not clearly visible. Specialists use their experience in anatomy to diagnose a tumor by absence of expected structures in the images. Naturally, undertaking such diagnosis is very difficult for an automated system. This chapter proposes an algorithm that is based on a combination of the manual diagnosis principles along with nature-inspired image processing techniques and artificial neural networks (ANN) to assist in the preliminary diagnosis of tumors affecting the bile ducts in the liver. The results obtained show over 88% success rate of the system developed using an ANN with the multi-layer perceptron (MLP) architecture, in performing the difficult automated preliminary detection of the tumors, even in the robust clinical test images with other biliary diseases present.

Detection of Eye Diseases using Image Processing and Artificial Neural Networks

2020 ◽  
pp. 15-20
Author(s):  
K. Sujatha ◽  
V. Srividhya ◽  
V. Karthikeyan ◽  
L. Madheshwaran ◽  
N. P. G. Bhavani
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Artificial Neural Networks ◽  
Eye Diseases ◽  
Artificial Neural
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