Breast Cancer Detection using ML

This personal project incorporates a machine learning model to detect breast cancer using a dataset by scikit-learn. By using Logistic Regression the model is trained to classify tumors to either a malignant (cancerous) class or a benign (non-cancerous) class, offering reliable predictions for simple binary medical classification tasks.

Abstract— This paper presents a machine learning (ML) method for detection and visual analysis of invasive ductal carcinoma (IDC) locations in whole slide images (WSI) of breast cancer. Machine learning is an artificial intelligence approach that learns from the experience consisting of computational methods and statistics to learn information directly from the dataset for modeling the relationships in data. It is a similar approach to how the human brain works by interpreting features such as representative layers.

Breast Cancer Detection in ML with Web End Deployment using Flask

This is a project using the Wisconsin Breast Cancer (Diagnostic) dataset from the UCI Machine Learning Repository. link: https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Diagnostic) I will compare different machine learning models (Logistic Regression, Support Vector Machines) to see what would provide the best classification results in differentiating malignant tumors from benign tumors.

Breast Cancer Detection using Supervised Machine Learning with SVM

Breast Cancer Detection and Prediction using Machine Learning ... Project: Research on Medical Domain using AI and ML ... allowing for more effective treatment to be used and reducing the risks of death from breast cancer.

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Methods for anomaly detection (outlier detection) on UCI ML Breast Cancer Wisconsin (Diagnostic) dataset

Multiple Disease Prediction System: An ML-based tool for early disease detection (Diabetes, Heart, Parkinson’s, Liver, Hepatitis, Lung Cancer, Kidney, Breast Cancer). Uses a Streamlit interface with trained models (.sav, .json) for risk prediction. Includes a Healthcare Chatbot for assistance.

Breast Cancer Detection using scikit-learn's Logistic Regression: A high-accuracy ML project utilizing pandas and numpy. Preprocesses Breast Cancer Wisconsin (Diagnostic) Dataset, applies feature engineering, and trains Logistic Regression model. Achieves 92.98% accuracy for reliable breast cancer diagnosis.

Breast Cancer Detection Using Machine Learning Classifier Goal of this ML project : I have extracted features of breast cancer patient cells and normal person cells then I create an ML model to classify malignant and benign tumor. To complete this ML project i used the supervised machine learning classifier algorithm. Author: Mannai Mohamed Mortadha

Breast cancer detection using ML

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GitHub repository showcasing Machine Learning code: KNN, KMeans, Random Forest, Decision Tree, Apriori, Conflict Serializable, Naive Bayes used for skin detection and UCI dataset evaluation to check accuracy. Extensively tested on reliable datasets like breast_cancer and iris, providing valuable insights for ML training and testing.

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Breast Cancer Detection using ML

Breast Cancer detection using knn classifier got 95% accurate model

Breast Cancer Detection Using Machine Learning: An Investigation and Algorithm Development

"Machine Learning project for Breast Cancer Detection using the Breast Cancer Wisconsin Diagnosis dataset. Implements Logistic Regression, PCA, and EDA to classify cases as malignant or benign with high accuracy. Includes comprehensive visualizations, evaluation metrics, and predictive capabilities for new data."

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Mammo-masses-Project: Predict whether a mammogram mass is benign or malignant.....Apply several different supervised machine learning techniques to this data set, and see which one yields the highest accuracy as measured with K-Fold cross validation (K=10). What we Apply: Decision tree; Random forest; KNN; Naive Bayes; SVM; Logistic Regression; And, as a bonus challenge, a neural network using Keras.

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Detection/Prediction of Breast cancer among females using Classification machine learning algorithms .Breast cancer here are of two type Malignant and Benign.

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Breast Cancer Detection Machine Learning Model

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A Machine Learning (ML) model for breast cancer detection is designed to assist in early and accurate diagnosis of breast cancer by analyzing patient data and identifying whether a tumor is benign (non-cancerous) or malignant (cancerous). Early detection is critical since it increases treatment success rates and reduces mortality.

Breast cancer (BC) is one of the most common cancers among women in the world, representing the majority of new cancer cases and deaths related to cancer according to global statistics, causing it a severe public health concern in today’s society. For 2019, it was estimated earlier by the Canadian Cancer Statistics that 26,900 Canadian women will be diagnosed with breast cancer, and 5,000 will die of it. Breast cancer accounts for approximately 25% of new cases of cancer and 13% of all cancer deaths in Canadian women. 1 in 8 women are expected to develop breast cancer during their lifetime, and 1 in 33 will die of it. While it can also be found in men, male breast cancer is an infrequent occurrence. Breast cancer starts in the cells of the mammary gland. Breast tissue covers a larger area than just the breast, extending up to the collarbone and fromthe armpit to the breastbone. A prediction of breast cancer inan initial stage provides a higher possibility of its cure. It needs a breast cancer prediction tool that can classify a breast tumorwhether it is a malignant tumor or a benign tumor. Machine learning (ML) is widely recognised as a technique of choice in BC pattern classification and forecast modelling due to its unique advantages in critical feature detection from complex BC datasets. Classification and data mining methods are an effective way to classify data. This work aims to show the working of different machine learning algorithms and compare the results of their performance accuracy to present an effective method for the prediction of breast cancer.

A ML project utilizing CNN for breast cancer detection through image processing. Achieved an accuracy of 97% using a dataset from Kaggle, where images were manually structured and processed for feature extraction with CNN, followed by classification using SVM.