The MAMA-MIA Dataset: A Multi-Center Breast Cancer DCE-MRI Public Dataset with Expert Segmentations
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Updated
Apr 16, 2025 - Jupyter Notebook
The MAMA-MIA Dataset: A Multi-Center Breast Cancer DCE-MRI Public Dataset with Expert Segmentations
In this work, we propose a deterministic version of Local Interpretable Model Agnostic Explanations (LIME) and the experimental results on three different medical datasets shows the superiority for Deterministic Local Interpretable Model-Agnostic Explanations (DLIME).
Decision Tree Algorithm written in Python with NumPy and Pandas
Decision Tree classifier from scratch without any machine learning libraries
Medical Chatbot for Breast Cancer Care
Breast cancer diagnoses with four different machine learning classifiers (SVM, LR, KNN, and EC) by utilizing data exploratory techniques (DET) at Wisconsin Diagnostic Breast Cancer (WDBC) and Breast Cancer Coimbra Dataset (BCCD).
This repository contains machine learning programs in the Python programming language.
Submission for CL4HEALTH @ LREC-COLING 2024
Random Forest Algorithm written in Python using NumPy and Pandas
A machine learning-based web app that predicts whether a breast tumor is Benign or Malignant using 29 medical features. Users can input data manually or upload a PDF report for automatic feature extraction. Built with Flask, Bootstrap, and PyMuPDF.
Decision Tree Classification was explored on Breast Cancer Data.
This Program is for Prediction of Breast Cancer
XGBoost Model for Machine Learning implementation on breast cancer dataset in Python and churm modelling in R
Open Source Breast Cancer Research for Summer Research Program ASIME 2024 @ Adelphi University, New York. Data from the UV Irvine Machine Learning Repository.
Implementation of some classification and clustering methods
Experiments for machine learning lab (ETCS 402) have been added here.
L'analyse des composantes principales essaie de trouver les axes principaux qui sont des variables décorrélées qui décrivent au mieux nos données.
This is a SteamLit Web-App which delves in Exploratory Data Analysis with Iris, Breast-Cancer and Wine datasets using ML models like KNN's, SVM's and Random Forests
[Big Data Analytics] This analysis aims to observe which features are most helpful in predicting malignant or benign cancer and to see general trends that may aid us in model selection and hyper parameter selection.
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