AI project for Cancer Classification for Breast Cancer
Breast Cancer Classification using Machine Learning This project aims to develop a machine learning model to classify breast tumors as malignant or benign. The model is trained on a dataset of breast cancer patient data, which includes several features such as tumor size, texture, and shape. The project uses the scikit-learn machine learning library in Python.
It also involves developing algorithms that can accurately differentiate between benign and malignant tumors in breast tissue based on various data points. These data points can include features extracted from medical imaging such as mammography or ultrasound, patient demographics, family history, and genetic data.
The goal is to develop models that can accurately predict whether a tumor is cancerous or not, allowing for earlier detection and better treatment outcomes. Machine learning techniques such as logistic regression, decision trees, support vector machines, and deep learning neural networks are commonly used in breast cancer classification.
A key challenge in this field is ensuring that the models are both accurate and generalizable across different patient populations and medical settings. Additionally, it is important to consider the ethical implications of using machine learning in medical diagnosis and to ensure that patients' privacy and autonomy are respected throughout the process.