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- Amrita’s Fetal Cardiology learning ( AFCL) Module – Fetal Heart Hybrid - Fellowship
- B.Tech. in Artificial Intelligence (AI) and Data Science (Medical Engineering) - Undergraduate
Publication Type : Conference Paper
Publisher : Institute of Electrical and Electronics Engineers
Source : Proceedings of the 2019 IEEE International Conference on Communication and Signal Processing, ICCSP 2019, Institute of Electrical and Electronics Engineers Inc., p.725-729 (2019)
Keywords : Artificial intelligence, breast cancer, Classification (of information), Classification performance, Classifier performance, Clinical application, computer aided design, decision making, Decision support systems, Diseases, Image retrieval, Making decision, Mammogram, Mammographic images, Mammographic mass, mammography, Medical imaging, Search engines, X ray screens
Campus : Coimbatore
School : School of Engineering
Department : Electronics and Communication
Year : 2019
Abstract : Accurate diagnosis is pivotal for successful treatment for breast cancer. High chances of survival are possible, if malignancy is detected at an early stage. Mammography is the most efficient and widely accepted modality for screening breast cancer. In this paper we propose a decision support system based on image retrieval which retrieves similar pathology based mammographic images to serve the physician in the diagnosis of breast cancer. The work explores how to use the retrieved similar cases as references to improve the classification performance. The rationale is that by incorporating the closeness information for decision making improves classifier performance rather than making decision from whole database. Experiments were carried out on DDSM database utilizing 4300 images of breast cancer. The results demonstrated the effectiveness of proposed system and show the vitality for clinical applications. © 2019 IEEE.
Cite this Research Publication : K. Kiruthika, Vijayan, D., and Dr. Lavanya R., “Retrieval driven classification for mammographic masses”, in Proceedings of the 2019 IEEE International Conference on Communication and Signal Processing, ICCSP 2019, 2019, pp. 725-729.