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A Novel Method for Detecting Breast Cancer Location Based on Growing GA-FCM Approach

The main idea of this article is to provide a numerical diagnostic method for breast cancer diagnosis of the MRI images. To achieve this goal, we used the region's growth method to identify the target area. In the area's growth method, based on the similarity or homogeneity of the adjacent...

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Main Authors: Milad Abaspoor, Saeed Meshgini, Tohid Yousefi Rezaii, Ali Farzamnia
Format: Article
Language:English
Published: 2020
Subjects:
T Technology (General)
TA Engineering (General). Civil engineering (General)
Online Access:https://eprints.ums.edu.my/id/eprint/25660/1/A%20Novel%20Method%20for%20Detecting%20Breast%20Cancer%20Location%20Based%20on%20Growing%20GA-FCM%20Approach.pdf
https://eprints.ums.edu.my/id/eprint/25660/
https://doi.org/10.1109/ICCKE48569.2019.8964904
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spelling my.ums.eprints.256602020-07-22T03:37:28Z https://eprints.ums.edu.my/id/eprint/25660/ A Novel Method for Detecting Breast Cancer Location Based on Growing GA-FCM Approach Milad Abaspoor Saeed Meshgini Tohid Yousefi Rezaii Ali Farzamnia T Technology (General) TA Engineering (General). Civil engineering (General) The main idea of this article is to provide a numerical diagnostic method for breast cancer diagnosis of the MRI images. To achieve this goal, we used the region's growth method to identify the target area. In the area's growth method, based on the similarity or homogeneity of the adjacent pixels, the image is subdivided into distinct areas according to the criteria used for homogeneity analysis to determine their belonging to the corresponding region. In this paper, we used manual methods and use of FCM as the function of genetic algorithm fitness. The presented algorithm is performed for 212 healthy and 110 patients. Results show that GA-FCM method have better performance than hand method to select initial points. The sensitivity of presented method is 0.67. The results of the comparison of the fuzzy fitness function in the genetic algorithm with other technique show that the proposed model is better suited to the Jaccard index with the highest Jaccard values and the lowest Jaccard distance. Among the techniques, the presented works well because of the similarity of techniques and the lowest Jaccard distance. Values close to 0.9 are close to 0.8. 2020 Article PeerReviewed text en https://eprints.ums.edu.my/id/eprint/25660/1/A%20Novel%20Method%20for%20Detecting%20Breast%20Cancer%20Location%20Based%20on%20Growing%20GA-FCM%20Approach.pdf Milad Abaspoor and Saeed Meshgini and Tohid Yousefi Rezaii and Ali Farzamnia (2020) A Novel Method for Detecting Breast Cancer Location Based on Growing GA-FCM Approach. IEEE. https://doi.org/10.1109/ICCKE48569.2019.8964904
institution Universiti Malaysia Sabah
building UMS Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Sabah
content_source UMS Institutional Repository
url_provider http://eprints.ums.edu.my/
language English
topic T Technology (General)
TA Engineering (General). Civil engineering (General)
spellingShingle T Technology (General)
TA Engineering (General). Civil engineering (General)
Milad Abaspoor
Saeed Meshgini
Tohid Yousefi Rezaii
Ali Farzamnia
A Novel Method for Detecting Breast Cancer Location Based on Growing GA-FCM Approach
description The main idea of this article is to provide a numerical diagnostic method for breast cancer diagnosis of the MRI images. To achieve this goal, we used the region's growth method to identify the target area. In the area's growth method, based on the similarity or homogeneity of the adjacent pixels, the image is subdivided into distinct areas according to the criteria used for homogeneity analysis to determine their belonging to the corresponding region. In this paper, we used manual methods and use of FCM as the function of genetic algorithm fitness. The presented algorithm is performed for 212 healthy and 110 patients. Results show that GA-FCM method have better performance than hand method to select initial points. The sensitivity of presented method is 0.67. The results of the comparison of the fuzzy fitness function in the genetic algorithm with other technique show that the proposed model is better suited to the Jaccard index with the highest Jaccard values and the lowest Jaccard distance. Among the techniques, the presented works well because of the similarity of techniques and the lowest Jaccard distance. Values close to 0.9 are close to 0.8.
format Article
author Milad Abaspoor
Saeed Meshgini
Tohid Yousefi Rezaii
Ali Farzamnia
author_facet Milad Abaspoor
Saeed Meshgini
Tohid Yousefi Rezaii
Ali Farzamnia
author_sort Milad Abaspoor
title A Novel Method for Detecting Breast Cancer Location Based on Growing GA-FCM Approach
title_short A Novel Method for Detecting Breast Cancer Location Based on Growing GA-FCM Approach
title_full A Novel Method for Detecting Breast Cancer Location Based on Growing GA-FCM Approach
title_fullStr A Novel Method for Detecting Breast Cancer Location Based on Growing GA-FCM Approach
title_full_unstemmed A Novel Method for Detecting Breast Cancer Location Based on Growing GA-FCM Approach
title_sort novel method for detecting breast cancer location based on growing ga-fcm approach
publishDate 2020
url https://eprints.ums.edu.my/id/eprint/25660/1/A%20Novel%20Method%20for%20Detecting%20Breast%20Cancer%20Location%20Based%20on%20Growing%20GA-FCM%20Approach.pdf
https://eprints.ums.edu.my/id/eprint/25660/
https://doi.org/10.1109/ICCKE48569.2019.8964904
_version_ 1760230396991111168
score 13.211869

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