Year: 2024
Type: conference-paper
Abstract: Finding breast cancer early makes treatment much more likely to work and increases the chance of life. Engineering smart systems have become useful tools for finding breast cancer earlier thanks to improvements in technology. They make the diagnosis more accurate and faster. This essay talks about many technical methods for finding breast cancer early, with a focus on how machine learning, artificial intelligence (AI), and advanced imaging technologies can be used together. In particular, it looks at how to create smart systems that use data from mammograms, ultrasound, and magnetic resonance imaging (MRI) to find and describe breast problems in their early stages. The study also talks about how AI algorithms, like convolutional neural networks (CNNs) and deep learning models, can be used to automatically analyse pictures of breast tissue, cutting down on mistakes made by humans and making estimates that are more accurate. The addition of these technologies to smart tech and mobile apps makes breast cancer screening even easier to get and more accessible. This makes real-time analysis and early action possible. The study also talks about the possibility of multi-modal data fusion, which means mixing different imaging methods to make cancer diagnosis more sensitive and specific. Creating these kinds of smart tools could change the way breast cancer is diagnosed by making it cheaper, more accurate, and more individualised for each patient. Lastly, social issues, data protection, and the future of these smart systems are talked about to make sure they can be used in real healthcare situations.
Authors: Abhijeet Nashte, Nitin Rakesh, Gendlal Vaidya, Anita Yadav, Ganesh Thorat, Sanjay Kumar Sinha
Language: English
Topic: AI in cancer detection
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4413911541