Breast Cancer Projects for M.E, M.Tech, Masters, MS abroad, and PhD students. These Breast Cancer ieee projects are implemented with future work and extension for final year project submission with research paper publishing. These research projects guide final year students to learn, practice, and complete their academic submissions successfully. Each project includes complete source code, project report, PPT, a tutorial, documentation, and a research paper.
Latest Breast Cancer Projects
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An Efficient Deep Neural Network to Classify Large 3D Images With Small Objects
This project creates a smart computer program that can analyze large 3D medical images, like mammograms, to detect cancer. It works efficiently without needing huge computer power. The program also shows which parts of the image influenced its decision. It performs well on real patient data and can generalize to other hospitals. -
Center-Focused Affinity Loss for Class Imbalance Histology Image Classification
This project focuses on helping doctors detect cancer early by analyzing medical images of tissues. The researchers created a new computer method that can better identify cancer cells, even when the data is uneven or hard to read. Their approach improves accuracy compared to existing techniques and works well on breast and colon cancer images. It aims to make cancer diagnosis faster and more reliable. -
LYSTO The Lymphocyte Assessment Hackathon and Benchmark Dataset
This project, called LYSTO, organized a fast-paced competition to count immune cells called lymphocytes in cancer tissue images. Participants had only a few hours to develop methods to analyze colon, breast, and prostate cancer samples. The results showed some methods matched expert pathologists in accuracy. The dataset and evaluation tools are now available online for further research. -
A Transfer Learning Approach to Breast Cancer Classification in a Federated Learning Framework
This project uses artificial intelligence to help detect breast cancer from medical images. It keeps patient data private by using federated learning, which trains models without sharing personal data. The system improves accuracy by enhancing images, balancing the data, and using advanced AI models. Tests show it performs better than traditional methods and can be used in healthcare safely.
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