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Presentation of Novel Hybrid Algorithm for Detection and Classification of Breast Cancer Using Growth Region Method and Probabilistic Neural Network Publisher Pubmed



Isfahani ZN1 ; Jannatdastjerdi I2 ; Eskandari F1 ; Ghoushchi SJ3 ; Pourasad Y4
Authors
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Authors Affiliations
  1. 1. Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
  2. 2. Department of Radiology, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
  3. 3. Department of Industrial Engineering, Urmia University of Technology, Urmia, Iran
  4. 4. Department of Electrical Engineering, Urmia University of Technology, Urmia, Iran

Source: Computational Intelligence and Neuroscience Published:2021


Abstract

Mammography is a significant screening test for early detection of breast cancer, which increases the patient's chances of complete recovery. In this paper, a clustering method is presented for the detection of breast cancer tumor locations and areas. To implement the clustering method, we used the growth region approach. This method detects similar pixels nearby. To find the best initial point for detection, it is essential to remove human interaction in clustering. Therefore, in this paper, the FCM-GA algorithm is used to find the best point for starting growth. Their results are compared with the manual selection method and Gaussian Mixture Model method for verification. The classification is performed to diagnose breast cancer type in two primary datasets of MIAS and BI-RADS using features of GLCM and probabilistic neural network (PNN). Results of clustering show that the presented FCM-GA method outperforms other methods. Moreover, the accuracy of the clustering method for FCM-GA is 94%, as the best approach used in this paper. Furthermore, the result shows that the PNN methods have high accuracy and sensitivity with the MIAS dataset. © 2021 Zeynab Nasr Isfahani et al.
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