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This program is designed to predict two severity of abnormalities associated with breast cancer cells: benign and malignant. Mammograms from MIAS is preprocessed and features are extracted using the pre-trained CNN.

Data challenge for breast cancer detection

中国农业大学2022级数据科学与大数据技术专业本科毕业设计项目。 Graduation project of the 2022 Data Science and Big Data Technology undergraduate program at China Agricultural University.

XGBoost and data cleaning on an imbalanced dataset

Undergraduate Thesis Project- Dimensionality Reduction Based Method for the Detection of Breast Cancer

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Automated detection and classification system for breast cancer metastases in whole-slide images of histological lymph node section

Breast Cancer Detection using python and machine learning

In this Project multiple ML Algorithm were used

This repo consists notebook which includes code to detect breast cancer pretty minutely.

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Breast Cancer Detection using Machine Learning and Data Analysis with Research Paper (Late 2019)

An CAD system to preprocess mammograms and extract texture features from it. Computer Vision library (OpenCV) was used to analyze the images and GLCM (Gray-level Co-Cooccurrence matrix) for feature extraction after apply different filters including Roberts for texture detection, which then transformed into Pandas DataFrame.

I implemented some machine learning algorithms to detect breast cancer

BreastCancerDetection project includes logistic regression as one of the machine learning algorithms used to detect breast cancer. Logistic regression is a binary classification algorithm that predicts the probability of an event occurring based on input features.

A ML model to classify Malignant and Benign tumors with an accuracy of 98.24%

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Here I have performed my exploratory data analysis and predicted if a person have benign or malignant tumor. I have trained two different models to predict the type of tumor. I have used K-NN and Logistic Regression. With K-NN i have got an accuracy of around 96.5% and with Logistic Regression i have got an accuracy around to 95.5%. The dataset was imbalanced so I have balanced the dataset and it contained some of outliers so i had detected the outliers and removed them. I have performed hyperparameter optimization and found the best value for k in K-NN and best value for C in Logistic Regression.Since the dataset dimmentionality was not very large hence i haven't performed dimentionality reduction.

This repository contains the code and analysis for a machine learning project aimed at classifying tumors as either malignant or benign using the Breast Cancer Wisconsin (Diagnostic) Dataset. The project involves the use of several classification algorithms, including Support Vector Machines (SVM), Decision Trees, and Random Forests

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Breast Cancer prediction with Machine Learning

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Project for my Machine Learning Course at Birzeit University.