How to Improve Medical Image Diagnosis through Association Rules: The IDEA Method

2008 ◽  
Author(s):  
Marcela Xavier Ribeiro ◽  
Agma Juci Machado Traina ◽  
Caetano Traina Jr ◽  
Natalia Abdala Rosa ◽  
Paulo Mazzoncini de Azevedo Marques
Keyword(s):  
Association Rules ◽  
Medical Image ◽  
Medical Image Diagnosis ◽  
Image Diagnosis

A New Method for Osteosarcoma Recognition Based on Bayesian Classifier

2014 ◽  
Vol 543-547 ◽  
pp. 2901-2904
Author(s):  
Wen Bo Huang ◽  
Yun Ji Wang
Keyword(s):  
Bayesian Network ◽  
Medical Image ◽  
Feature Vector ◽  
Probability Model ◽  
Image Features ◽  
New Method ◽  
Training Samples ◽  
Recognition Probability ◽  
Medical Image Diagnosis ◽  
Image Diagnosis

In order to deal with the complexity and uncertainty in medical image diagnosis of osteosarcoma, we proposed a new method based on Bayesian network, and first applied it to recognize osteosarcoma. A new multidimensional feature vector composed of both biochemical indicator and the quantized image features is defined and used as input to the Bayesian network, so as to establish a more accurate and reliable osteosarcoma recognition probability model. Experimental results demonstrate the effective of our method, there are 50 training samples and 30 testing samples, and the accuracy is up to 86.67%, which close to the expert diagnosis.

408 Verification and Proposal of Medical Image Diagnosis System Using Neural Networks

2007 ◽  
Vol 2007 (0) ◽  
pp. 155-156
Author(s):  
Kazuya Kubo ◽  
Hironobu Satoh ◽  
Yuhki Shiraishi ◽  
Fumiaki Takeda ◽  
Keiji Inoue
Keyword(s):  
Neural Networks ◽  
Medical Image ◽  
Diagnosis System ◽  
Medical Image Diagnosis ◽  
Image Diagnosis

scholarly journals Medical Image Segmentation by Fish Schooling Algorithm and Neural Network

2021 ◽  
Vol 7 (10) ◽  
pp. 55-60
Author(s):  
K V Sandeep, Manoj Dandamudi and P Dhanusha
Keyword(s):  
Neural Network ◽  
Medical Image ◽  
Tumor Detection ◽  
Medical Image Segmentation ◽  
Detection Accuracy ◽  
Medical Image Diagnosis ◽  
Image Diagnosis ◽  
Tumor Region ◽  
Fish Schooling ◽  
Genetic Feature Selection

Medical image diagnosis by machine decrease the doctor load and increases the efficiency of treatment as well. Many of diagnosis process depends on chemical data and some are depend on digital images. This work focus on brain tumor medical image diagnosis by segmenting the tumor region in the image. For tumor detection neural network was trained by the model. Selected features extract from the image by fish schooling genetic algorithm for training of neural network It was obtained that fish schooling based genetic feature selection has increases the detection accuracy of trained model. Experiment was done on real dataset and results compared with existing techniques of tumor detection from MRI images.

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