Breast cancer has the second highest mortality rate in women next to lung cancer. As per clinical statistics, 1 in every 8 women is diagnosed with breast cancer in their lifetime. However, periodic clinical check-ups and self-tests help in early detection and thereby significantly increase the chances of survival. Invasive detection techniques cause rupture of the tumor, accelerating the spread of cancer to adjoining areas. Hence, there arises the need for a more robust, fast, accurate, and efficient non-invasive cancer detection system. Early detection can give patients more treatment options. In order to detect signs of cancer, breast tissue from biopsies is stained to enhance the nuclei and cytoplasm for microscopic examination. Then, pathologists evaluate the extent of any abnormal structural variation to determine whether there are tumors. Architectural Distortion (AD) is a very subtle contraction of the breast tissue and may represent the earliest sign of cancer. Since it is very likely to be unnoticed by radiologists, several approaches have been proposed over the years but none using deep learning techniques. AI will become a transformational force in healthcare and soon, computer vision models will be able to get a higher accuracy when researchers have the access to more medical imaging datasets. The application of machine learning models for prediction and prognosis of disease development has become an irrevocable part of cancer studies aimed at improving the subsequent therapy and management of patients. The application of machine learning models for accurate prediction of survival time in breast cancer on the basis of clinical data is the main objective. We have developed a computer vision model to detect breast cancer in histopathological images. Two classes will be used in this project: Benign and Malignant

Breast cancer is one of the most common cancers among women worldwide, representing the majority of new cancer cases and cancer-related deaths according to global statistics, making it a significant public health problem in today’s society. The early diagnosis of it can improve the prognosis and chance of survival significantly, as it can promote timely clinical treatment to patients. Further accurate classification of benign tumors can prevent patients undergoing unnecessary treatments. Thus, the correct diagnosis of Breast Cancer and classification of patients into malignant or benign groups is the subject of much research. Because of its unique advantages in critical features detection from complex Breast Cancer datasets, machine learning (ML) is widely recognized as the methodology of choice in Breast cancer pattern classification and forecast modelling. Classification and data mining methods are an effective way to classify data. Especially in medical field, where those methods are widely used in diagnosis and analysis to make decisions. Because we are categorizing whether the tissue is cancerous or benign, we will train multiple Tree-based models for this procedure. We’ll experiment with hyper-parameters to see if we can enhance the accuracy. Try to solve the problem using the approach outlined below. For further information on each feature, consult the data dictionary. Decision trees (DTs) form the basis of ensemble algorithms in machine learning. These are powerful algorithms that can fit complex data. In this project, our focus is on understanding the core concepts of the Decision Tree for healthcare analysis, followed by understanding the different ensemble techniques.

A machine learning model predicting breast tumors as malignant (cancerous) or benign (non-cancerous) using the Breast Cancer Wisconsin dataset for improved early detection.

A machine learning model for classifying breast cancer as benign or malignant using diagnostic features. Trained on the Breast Cancer Wisconsin dataset with algorithms like KNN, SVM, or Random Forest. This project aims to support early detection and improve diagnostic accuracy.

This project uses the Breast Cancer Wisconsin dataset to build a Logistic Regression model for classifying tumors as malignant or benign. The goal is to apply supervised machine learning techniques to help in early cancer detection

This project implements a machine learning model using Support Vector Classifier (SVC) with GridSearchCV for hyperparameter tuning to enhance breast cancer classification. The approach aims to improve early detection accuracy and aid in precise diagnosis of malignant and benign tumors using the Wisconsin Breast Cancer Diagnostic (WBCD) dataset.

A machine learning project for early detection of breast cancer tumors using the XGBoost algorithm. This project preprocesses the dataset, trains an XGBoost classifier, evaluates model performance, and predicts tumor presence with high accuracy.

Breast cancer is one of the commonest cause of cancer deaths in women. It starts developing when threatening bumps start forming from the breast cells, and unfortunately most diagnoses happen in later stages, thus resulting in low chances of survival for the patient. So for early detection and prognosis, it is necessary to detect the benign or threatening nature of the bumps. In this paper, Artificial Neural Networks (ANN) and Decision Tree (DT) classifiers are used to develop a machine learning (ML) model using the Wisconsin diagnostic breast cancer (WDBC) dataset, in order to evaluate the attributes of a breast cancer development at beginning phases and classify it as malignant or benign. In the proposed scheme, feature selection and feature extraction are done to extract statistical features from the dataset and comparison between the models is provided based on their performance to identify the most suitable approach for diagnosis. The dataset apportioned into various arrangements of train-test split. The presentation of the framework is estimated, depending on accuracy, sensitivity, specificity, precision, and recall. The binary classification problem achieved a maximum accuracy of 98.55%.

Breast Cancer Dataset for Machine Learning This repository contains a labeled breast cancer dataset designed for classification and predictive modeling tasks in medical data analysis. The dataset is suitable for machine learning and deep learning approaches focused on breast cancer diagnosis and risk prediction.

University Project - Machine learning-based system for the early detection of breast cancer using a well-known dataset.

A machine learning-based approach for early detection and classification of breast cancer, Using machine learning algorithms and medical datasets to improve diagnostic accuracy.

This repository contains a data science project focused on predicting breast cancer using machine learning techniques. The project employs a dataset with various features related to breast cancer tumors, aiming to build predictive models for early detection and classification.

This repository features a machine learning model for breast cancer detection, utilizing logistic regression to classify tumors as malignant or benign. Developed in Google Colab using the Wisconsin Breast Cancer Dataset from scikit-learn, this project demonstrates a simple and effective tool for early detection.

This project predicts breast cancer using the Wisconsin Breast Cancer Diagnostic dataset. Features from cell nuclei are analyzed to classify tumors as benign or malignant. A machine learning model was built to enable early and accurate detection for improved treatment outcomes.

This project is a Breast Cancer Detection System that helps in early detection of cancer. The system enables us to analyse if it is a malignant cancer or benign cancer for early treatment . The project is built using Machine Learning Techniques . We have trained the model with dataset based on which the model makes detection.

This project focuses on building an intelligent system for early detection of breast cancer using Machine Learning algorithms. The Wisconsin Breast Cancer Dataset (WBCD) was used for training and testing multiple models including Random Forest, Logistic Regression, Decision Tree, Support Vector Machine (SVM), and K-Nearest Neighbors (KNN).

This project analyzes the Coimbra Breast Cancer dataset machine learning techniques (Logistic Regression, Random Forest, SVM, etc.). It includes data preprocessing, EDA, model tuning via Grid Search, and evaluation using accuracy and AUC. The aim is to identify key biomarkers for early breast cancer detection.

The detection of credit card fraud project by CodeClause involves using Python and Machine learning algorithms to develop a system for detecting fraudulent credit card transactions. The Breast Cancer Classification project uses Python and CNNs on IDC dataset to classify breast cancer and learn about early detection and preventive measures.

Developed a machine learning-based system for early breast cancer detection using the Breast Cancer Wisconsin dataset. Preprocessed data with feature selection and imputation. Implemented SVM, Decision Trees, Naïve Bayes, and k-NN, optimizing accuracy with ROC curves. Built a MATLAB GUI for real-time diagnosis.

A machine learning–based system for early breast cancer detection using the Breast Cancer Wisconsin dataset. Data preprocessing, feature scaling, and train–test split are applied. Logistic Regression and Decision Tree classify tumors as benign or malignant and are evaluated using accuracy, precision, recall, F1-score, and confusion matrix.

This project implements a machine learning pipeline for early cancer detection using the Breast Cancer Wisconsin dataset from scikit-learn. The goal is to demonstrate how AI can assist clinicians by providing reliable, interpretable classification of cancerous tumors from diagnostic data.

This project focuses on developing a highly accurate and robust machine learning pipeline for the early detection of breast cancer using the Wisconsin Diagnostic dataset. The primary goal was to enhance diagnostic accuracy, specifically minimizing False Negatives to ensure early intervention.

Developed a Breast Cancer Detection system using Machine Learning to classify tumors as Benign or Malignant. Leveraged Python and Scikit-learn to train models on the Wisconsin Diagnostic Breast Cancer dataset. Achieved high accuracy through data normalization and feature selection, providing a reliable tool for early medical diagnosis.

OncoVision is a machine learning tool for diagnosing breast cancer by classifying tumors as benign or malignant using clinical features from FNA images. It utilizes the WBCD dataset to support early, accurate detection and improve patient outcomes.

This project focuses on developing machine learning models for early breast cancer detection using logistic regression, decision tree, and random forest algorithms. Leveraging a Kaggle dataset comprising features extracted from breast tissue images, the report presents comprehensive data preprocessing, model implementation, and evaluation metrics.

This project focuses on developing machine learning models for early breast cancer detection using logistic regression, decision tree, and random forest algorithms. Leveraging a Kaggle dataset comprising features extracted from breast tissue images, the report presents comprehensive data preprocessing, model implementation, and evaluation metrics

This project develops a machine learning pipeline to classify breast tumors as benign or malignant using the Breast Cancer Wisconsin Dataset. It applies LASSO for feature selection and utilizes classifiers such as KNN, Naive Bayes, SVM, Decision Tree, and Random Forest to optimize diagnostic accuracy, enhancing early detection.

An optimized machine learning model in Python for early breast cancer detection from a given dataset, utilizing advanced preprocessing, classification techniques, and optimization methods. The project aims to deploy a scalable system for real-time clinical use, emphasizing accuracy(97%), efficiency.

This project aims to develop a robust and accurate system for breast cancer detection using various machine learning algorithms. By leveraging comprehensive datasets, the goal is to build predictive models that can assist medical professionals in early diagnosis, thereby improving patient outcomes.

A machine learning system classifying breast tumors as malignant or benign using the Wisconsin dataset. Employs advanced preprocessing with Yeo-Johnson transformation and PCA for dimensionality reduction. Multiple ML models including SVM with RBF and Polynomial kernels were evaluated to achieve accurate early cancer detection.