Disciplines
Artificial Intelligence and Robotics | Computer Sciences
Abstract (300 words maximum)
Last year, more than 240 thousand women in the United States were diagnosed with breast cancer. These patients are benefitting from decades of data that have been collected by cancer research institutions around the world. Tissue samples are analyzed and cataloged by these institutions, and several facilities like the University of Wisconsin are sharing this historical data to promote the advancement of new cancer treatments. Deep learning and neural network models are being built for this data to help doctors diagnose faster and design treatment options for patients by comparing their tissue samples with these historical datasets. We will use the data from the Wisconsin cancer study to evaluate different deep learning models and analyze their effectiveness in the classification of malignant versus non-malignant tissue samples. By sharing and discussing the models developed during this study, we hope to raise awareness of breast cancer research and raise educational interest in exploring AI-based deep learning techniques to assist with cancer identification.
Academic department under which the project should be listed
CCSE - Computer Science
Primary Investigator (PI) Name
Md Abdullah Al Hafiz Khan
Included in
Exploring Neural Networks for Breast Cancer Tissue Classification
Last year, more than 240 thousand women in the United States were diagnosed with breast cancer. These patients are benefitting from decades of data that have been collected by cancer research institutions around the world. Tissue samples are analyzed and cataloged by these institutions, and several facilities like the University of Wisconsin are sharing this historical data to promote the advancement of new cancer treatments. Deep learning and neural network models are being built for this data to help doctors diagnose faster and design treatment options for patients by comparing their tissue samples with these historical datasets. We will use the data from the Wisconsin cancer study to evaluate different deep learning models and analyze their effectiveness in the classification of malignant versus non-malignant tissue samples. By sharing and discussing the models developed during this study, we hope to raise awareness of breast cancer research and raise educational interest in exploring AI-based deep learning techniques to assist with cancer identification.