Implementation of Machine Learning Approaches for Breast Cancer Prediction

• Published in: Turkish journal of computer and mathematics education Vol. 12; no. 1S; pp. 73 - 79
• Main Authors: Hausalmal, Komal, Kshirsagar, J P
• Format: Journal Article
• Language: English
• Published: Gurgaon Ninety Nine Publication 11.04.2021
• Subjects: Artificial Intelligence; Bioinformatics; Biopsy; Breast cancer; Breast diseases; Cancer; Cytology; Database Management Systems; Datasets; Division; Feature extraction; Histology; Histopathology; Literary Devices; Machine learning; Pathology; Radiology
• ISSN: 1309-4653; 1309-4653
• DOI: 10.17762/turcomat.v12i1S.1562

The grouping of bosom malignant growth has been the subject of enthusiasm for the fields  of  medicinal  services and bioinformatics, in light of the fact that it is the subsequent primary explanation of disease related passings in ladies. Bosom malignancy can be investigated utilizing a biopsy where tissue is wiped out and concentrated under magnifying instrument. The distinguishing proof of issue depends on the capability and experienced of the histopathologists, who will consideration for unusual cells. Be that  as  it  may,  if  the  histopathologist  isn’t  all around prepared or encountered, this may prompt wrong finding. With the ongoing suggestion in picture handling and AI space, there is an enthusiasm for test to build up a solid example acknowledgment based structure to improve the nature of finding. In this work, the picture highlight extraction approach and AI approach is utilized for the grouping of bosom disease utilizing histology pictures into threatening. The preprocessing on the picture is done using histopathological picture after that apply feature extraction and classify the final result using SVM and Naive Bayes Classification techniques.