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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\u2010GA 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\u2010RADS using features of GLCM and probabilistic neural network (PNN). Results of clustering show that the presented FCM\u2010GA method outperforms other methods. Moreover, the accuracy of the clustering method for FCM\u2010GA 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.<\/jats:p>","DOI":"10.1155\/2021\/5863496","type":"journal-article","created":{"date-parts":[[2021,6,21]],"date-time":"2021-06-21T16:35:52Z","timestamp":1624293352000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Presentation of Novel Hybrid Algorithm for Detection and Classification of Breast Cancer Using Growth Region Method and Probabilistic Neural Network"],"prefix":"10.1155","volume":"2021","author":[{"given":"Zeynab Nasr","family":"Isfahani","sequence":"first","affiliation":[]},{"given":"Iman","family":"Jannat-Dastjerdi","sequence":"additional","affiliation":[]},{"given":"Fatemeh","family":"Eskandari","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3665-9010","authenticated-orcid":false,"given":"Saeid Jafarzadeh","family":"Ghoushchi","sequence":"additional","affiliation":[]},{"given":"Yaghoub","family":"Pourasad","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2021,6,21]]},"reference":[{"unstructured":"BharatiS. 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