Please use this identifier to cite or link to this item: https://rda.sliit.lk/handle/123456789/2552
Title: Mammogram-Based Cancer Detection Using Deep Convolutional Neural Network
Authors: Thudawehewa, H
Silva, C
Rathnayake, p
Thudawehewa, T
Keywords: Mammogram-Based
Cancer Detection
Deep Convolutional
Neural Network
Issue Date: 2-Dec-2021
Publisher: IEEE
Citation: H. Thudawehewa, C. Silva, P. Rathnayake and T. Thudawehewa, "Mammogram-Based Cancer Detection Using Deep Convolutional Neural Network," 2021 21st International Conference on Advances in ICT for Emerging Regions (ICter), 2021, pp. 195-200, doi: 10.1109/ICter53630.2021.9774783.
Series/Report no.: 2021 21st International Conference on Advances in ICT for Emerging Regions (ICter);Pages 195-200
Abstract: Breast cancer is now a common health problem among most women. Breast cancer is the world’s second-largest cause of mortality for women, and it affects mostly women over the age of 50. The major reasons are that most women do not have proper knowledge about breast diseases/conditions, and the inability to detect abnormalities in the initial stages. A mammogram is one of the best imaging modalities recommended by doctors to diagnose breast cancers. Consultant radiologists are necessary for the identification of those breast pathologies by mammogram images. For a human, it takes some time to read and have an opinion about the condition. Also, the pandemic situation makes the diagnosis processes even more difficult due to the unavailability of doctors and other medical staff. Deep learning approaches are applied for breast cancer detection, and it helps radiologists to identify breast pathologies quickly and accurately. In this work, the mammogram images are collected using MIAS, DDSM, and INbreast databases. The proposed system identifies the location of the lump within the breast, if the lump is malignant or benign, the size of the lump, and the state of the nipple (It is abnormal or not). Convolutional Neural Network (CNN) method for classifying screening mammograms obtained outstanding performance compared to the previous methods. This CNN method produces 96.5% accuracy for breast tumor classification and produces the 80% accuracy for nipple classification.
URI: http://rda.sliit.lk/handle/123456789/2552
ISSN: 2472-7598
Appears in Collections:Department of Information Technology-Scopes
Research Papers - IEEE
Research Papers - SLIIT Staff Publications
Research Publications -Dept of Information Technology

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