Menu
×
West Ashley Library
9 a.m. - 4 p.m.
Phone: (843) 766-6635
Main Library
9 a.m. - 6 p.m.
Phone: (843) 805-6930
Folly Beach Library
Closed for renovations
Phone: (843) 588-2001
John L. Dart Library
9 a.m. - 6 p.m.
Phone: (843) 722-7550
St. Paul's/Hollywood Library
9 a.m. - 5 p.m.
Phone: (843) 889-3300
Mt. Pleasant Library
9 a.m. – 6 p.m.
Phone: (843) 849-6161
Dorchester Road Library
9 a.m. - 6 p.m.
Phone: (843) 552-6466
Edgar Allan Poe/Sullivan's Island Library
9 a.m. - 6 p.m.
Phone: (843) 883-3914
John's Island Library
9 a.m. - 6 p.m.
Phone: (843) 559-1945
McClellanville Library
Closed for renovations
Phone: (843) 887-3699
Edisto Library
9 a.m. - 3 p.m.
Phone: (843) 869-2355
Wando Mount Pleasant Library
9 a.m. - 6 p.m.
Phone: (843) 805-6888
Otranto Road Library
9 a.m. - 6 p.m.
Phone: (843) 572-4094
Hurd/St. Andrews Library
9 a.m. - 6 p.m.
Phone: (843) 766-2546
Baxter-Patrick James Island
9 a.m. - 6 p.m.
Phone: (843) 795-6679
Bees Ferry West Ashley Library
9 a.m. - 6 p.m.
Phone: (843) 805-6892
Village Library
9 a.m. - 6 p.m.
Phone: (843) 884-9741
Keith Summey North Charleston Library
9 a.m. – 6 p.m.
Phone: (843) 744-2489
Mobile Library
9 a.m. - 5 p.m.
Phone: (843) 805-6909
Today's Hours
West Ashley Library
9 a.m. - 4 p.m.
Phone: (843) 766-6635
Main Library
9 a.m. - 6 p.m.
Phone: (843) 805-6930
Folly Beach Library
Closed for renovations
Phone: (843) 588-2001
John L. Dart Library
9 a.m. - 6 p.m.
Phone: (843) 722-7550
St. Paul's/Hollywood Library
9 a.m. - 5 p.m.
Phone: (843) 889-3300
Mt. Pleasant Library
9 a.m. – 6 p.m.
Phone: (843) 849-6161
Dorchester Road Library
9 a.m. - 6 p.m.
Phone: (843) 552-6466
Edgar Allan Poe/Sullivan's Island Library
9 a.m. - 6 p.m.
Phone: (843) 883-3914
John's Island Library
9 a.m. - 6 p.m.
Phone: (843) 559-1945
McClellanville Library
Closed for renovations
Phone: (843) 887-3699
Edisto Library
9 a.m. - 3 p.m.
Phone: (843) 869-2355
Wando Mount Pleasant Library
9 a.m. - 6 p.m.
Phone: (843) 805-6888
Otranto Road Library
9 a.m. - 6 p.m.
Phone: (843) 572-4094
Hurd/St. Andrews Library
9 a.m. - 6 p.m.
Phone: (843) 766-2546
Baxter-Patrick James Island
9 a.m. - 6 p.m.
Phone: (843) 795-6679
Bees Ferry West Ashley Library
9 a.m. - 6 p.m.
Phone: (843) 805-6892
Village Library
9 a.m. - 6 p.m.
Phone: (843) 884-9741
Keith Summey North Charleston Library
9 a.m. – 6 p.m.
Phone: (843) 744-2489
Mobile Library
9 a.m. - 5 p.m.
Phone: (843) 805-6909
Patron Login
menu
Item request has been placed!
×
Item request cannot be made.
×
Processing Request
Predicting and Classifying Breast Cancer Using Machine Learning.
Item request has been placed!
×
Item request cannot be made.
×
Processing Request
- Author(s): Alkhathlan L;Alkhathlan L; Saudagar AKJ; Saudagar AKJ
- Source:
Journal of computational biology : a journal of computational molecular cell biology [J Comput Biol] 2022 Jun; Vol. 29 (6), pp. 497-514. Date of Electronic Publication: 2021 Dec 09.- Publication Type:
Journal Article; Research Support, Non-U.S. Gov't- Language:
English - Source:
- Additional Information
- Source: Publisher: Mary Ann Liebert, Inc Country of Publication: United States NLM ID: 9433358 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1557-8666 (Electronic) Linking ISSN: 10665277 NLM ISO Abbreviation: J Comput Biol Subsets: MEDLINE
- Publication Information: Original Publication: New York, NY : Mary Ann Liebert, Inc., c1994-
- Subject Terms:
- Abstract: The proposed research work aims to develop a method to predict and classify breast cancer (BC) at an early stage. In this research, three models are developed, and their performance is compared against each other. The first model was built using one of the machine learning algorithms called support vector machine (SVM), the second model was built using a deep learning algorithm called convolutional neural networks (CNNs), and the third model combines CNNs with a transfer learning technique for delivering better results. The data set is provided by the BC Histopathological Image Classification (BreakHis). All models are trained on the training set with two main categories: benign tumor and malignant tumor. The malignant tumor category is divided into subsets of invasive carcinoma tumors and in situ carcinoma tumors. Furthermore, invasive carcinoma tumors are classified into grade 1, grade 2, or grade 3, where grade 3 is the highest and is more aggressive. The results show that the accuracies of biopsy image classification using SVM are 92%, the accuracy of CNN is 94%, and the accuracy of CNN using the transfer learning technique is 97%. The results of this research will be beneficial in the early diagnosis of BC and help doctors in making better decisions and medical interventions.
- Contributed Indexing: Keywords: breast cancer; classification; deep learning; machine learning; prediction
- Publication Date: Date Created: 20211209 Date Completed: 20220609 Latest Revision: 20220824
- Publication Date: 20240105
- Accession Number: 10.1089/cmb.2021.0236
- Accession Number: 34883032
- Source:
Contact CCPL
Copyright 2022 Charleston County Public Library Powered By EBSCO Stacks 3.3.0 [350.3] | Staff Login
No Comments.