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Detection of Benign and Malignant Breast Cancer with Deep Learning and Artificial Attention Networks
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Elham Kiani   , Maryam Bayati |
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Abstract: (198 Views) |
| Breast cancer is one of the most common and deadly types of cancer in women, and mammography is a reliable diagnostic method for early detection of breast cancer. In this research, we set out to use machine learning and neural networks to find a reliable solution to diagnose benign and malignant breast cancer. Because human diagnosis includes errors, and it is also used to avoid paying high medical costs, ease the work of eliminating human resources, and also to diagnose taste and prevent wasting time of doctors in viewing images and presenting results by them. Artificial intelligence excels in data recognition in large volumes, extracting relationships between them and discovering complex features that cannot be understood by the human brain. For this reason, every day is used more than the previous day in the diagnosis and treatment of the disease. In this research, by using CBMA, we increased the representational power of a convolutional neural network by drawing attention both in terms of channel and space in order to focus on informational regions and features and improve its recognition ability and overall performance. By using the proposed solution, we reached 63% and 0.09 percent of the recall accuracy criterion and F1 criterion |
Article number: 4 |
| Keywords: Neural Network, Machine Learning, Body damage |
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Full-Text [PDF 574 kb]
(113 Downloads)
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Type of Study: Research |
Subject:
Special
Received: 2024/03/7 | Accepted: 2025/02/7 | Published: 2025/02/8
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