Abstract
dc:description.abstract<p>Medical images are playing an increasingly important role in the prevention and diagnosis of diseases. Medical images often contain massive amounts of data. Professional interpretation usually requires a long time of professional study and experience accumulation by doctors. Therefore, the use of super storage and computing power in deep learning as a basis can effectively process a large amount of medical data. Breast cancer brings great harm to female patients, and early diagnosis is the most effective prevention and treatment method, so this project will create a new optimized breast cancer auxiliary diagnosis model based on ResNet. Analyze and process, realize medical aided diagnosis, and provide scientific diagnosis for breast cancer patients.</p>
Degree
thesis:*- Name thesis:degree_name
- Master of Science in Computer Science (MSCS)
- Level thesis:degree_level
- Thesis
- Discipline thesis:degree_discipline
- Computer Science
- Year dc:date.available
- 2020
Author and committee
dc:creator, dc:contributor.*- Author dc:creator
-
- Li, Yihong
- Contributors dc:contributor
-
- Dr. Mohammed Aledhari
- Dr. Chih-Cheng Hung
Subjects
dc:subject × 3Identifiers
dc:identifier.*- Repository record dc:identifier
- https://digitalcommons.kennesaw.edu/cs_etd/34
- OAI identifier oai:identifier
- oai:digitalcommons.kennesaw.edu:cs_etd-1039