Overview / Usage

The breast cancer classification project using neural network and deep learning is aimed at developing a predictive model to classify breast tumors as either benign or malignant based on the features present in their cell nuclei. This project involves collecting and processing a dataset of breast cancer cases, training a neural network model using deep learning techniques, and evaluating the model's accuracy in classifying new cases.

The problem being solved is the accurate classification of breast tumors, which can have significant implications for patient diagnosis and treatment. Misclassification of tumors can lead to delayed treatment, unnecessary interventions, and increased health risks. Therefore, an accurate and reliable model for classifying breast tumors can improve patient outcomes and reduce healthcare costs.

The project will involve exploring the dataset, preparing the data for analysis, building and training a neural network using TensorFlow, and evaluating the model's performance using metrics such as accuracy, precision, recall, and F1 score. The libraries used in this project include NumPy, Pandas, Matplotlib, Scikit-learn, and TensorFlow.

The research and development of this breast cancer classification model using deep learning techniques are useful in production in the field of medical diagnosis and treatment. Medical professionals can use the model to aid in their diagnosis and treatment of breast cancer, helping to ensure that patients receive the most appropriate and effective care. The model can also be incorporated into medical devices and software applications to provide real-time tumor classification, improving patient outcomes and reducing healthcare costs.

Technologies Used

oneDAL, oneDNN

Repository

https://github.com/shaarang123/Breast-Cancer-Classification/

Collaborators