Breast Cancer Prediction from Numerical Features from MRI

[murtaza-sadri-19]

Problem Description: Breast cancer is one of the most common types of cancer affecting women worldwide. Early detection and diagnosis are crucial for effective treatment and improved survival rates. Magnetic Resonance Imaging (MRI) is a non-invasive imaging modality that provides valuable information for breast cancer diagnosis. However, interpreting MRI images and identifying potential tumors can be a time-consuming and challenging task for radiologists. This is where machine learning can help.

The problem this solution aims to address is the development of a predictive model that can accurately classify breast cancer from numerical features extracted from MRI images in form of csv file. The goal is to create a reliable and efficient tool that can assist radiologists in diagnosing breast cancer, reducing the likelihood of human error and improving patient outcomes.

Model Description: To solve this problem, we plan to use a supervised learning approach, specifically an ensemble model with a Ridge classifier. The model will be trained on a dataset of numerical features extracted from MRI images, along with corresponding labels indicating the presence or absence of breast cancer. Note : Wisconsin Dataset is used. The technical details of the model are as follows:

• The model will be implemented using Python and the scikit-learn library.

• We will use an ensemble approach, combining multiple models to improve the overall performance.

• The ensemble model will be tuned using GridSearchCV, a technique that allows us to perform hyperparameter tuning and model selection.

• The best-performing model, as determined by GridSearchCV, is a Ridge classifier.

• The Ridge classifier is a type of linear regression model that uses L2 regularization to prevent overfitting.

• We have chosen an ensemble approach with a Ridge classifier because it provides a robust and accurate way to classify breast cancer from numerical features extracted from MRI images.

Expected Outcome: The impact of this model will be:

• Improved diagnostic accuracy: The model will assist radiologists in making more accurate diagnoses, reducing the likelihood of false positives and false negatives.
• Increased efficiency: The model will automate the process of analyzing MRI images, freeing up radiologists to focus on more complex cases.
• Enhanced patient care: The model will enable clinicians to make more informed treatment decisions, leading to better patient outcomes.

[RandomSummer]

@yashasvini121 I have already worked on a Breast Cancer Model using SVM with an accuracy of ~96%. If possible can assign me the task?