Searching for just a few words should be enough to get started. If you need to make more complex queries, use the tips below to guide you.
Article type: Research Article
Authors: Anbumani, A.a; * | Jayanthi, P.b
Affiliations: [a] Department of Artificial Intelligence and Data Science, Rajalakshmi Institute of Technology, Namakkal, Tamilnadu, India | [b] Department of Computer Science and Engineering, Kongu Engineering College, Erode, Tamil Nadu
Correspondence: [*] Corresponding author. A. Anbumani, M.C.A., M.E., (Ph.D)., Assistant Professor, Department of Artificial Intelligence and Data Science, Rajalakshmi Institute of Technology, Namakkal Dist., Tamilnadu, India. E-mail: [email protected].
Abstract: GLOBOCAN 2020 states that, after lung cancer, breast cancer is the most common cancer worldwide, affecting many women [1]. AI-based computer-assisted detection/diagnosis techniques can assist radiologists in diagnosing breast cancer earlier. Mammography is one of the most widely used and effective methods for detecting and treating breast cancer. This research proposes a customised deep-learning model for breast cancer categorization. To effectively categorise the breast cancer mammography image, two customised CNN models are proposed. Three real-time datasets such as MIAS, CBIS-DDSM, and INbreast were used to evaluate the efficacy of the proposed categorization strategy. The results show that the proposed method effectively classifies the image and obtains 98.78%, 97.84% and 96.92% accuracy for the datasets MIAS, INbreast and CBIS-DDSM.
Keywords: Breast cancer, CNN, deep learning, mammography, classification
DOI: 10.3233/JIFS-232896
Journal: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
IOS Press, Inc.
6751 Tepper Drive
Clifton, VA 20124
USA
Tel: +1 703 830 6300
Fax: +1 703 830 2300
[email protected]
For editorial issues, like the status of your submitted paper or proposals, write to [email protected]
IOS Press
Nieuwe Hemweg 6B
1013 BG Amsterdam
The Netherlands
Tel: +31 20 688 3355
Fax: +31 20 687 0091
[email protected]
For editorial issues, permissions, book requests, submissions and proceedings, contact the Amsterdam office [email protected]
Inspirees International (China Office)
Ciyunsi Beili 207(CapitaLand), Bld 1, 7-901
100025, Beijing
China
Free service line: 400 661 8717
Fax: +86 10 8446 7947
[email protected]
For editorial issues, like the status of your submitted paper or proposals, write to [email protected]
如果您在出版方面需要帮助或有任何建, 件至: [email protected]