breastcancer-classification

Classification of Breast Cancer diagnosis Using Support Vector Machines

Classifying malignant and benign tumors using Neural Networks 🔬

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.

Python Data Analytics, Machine Learning & Natural Language Processing

Artificial Neural Network - Wisconsin Breast Cancer Detection

Breast Cancer lump classification using CNN

Prediction of Benign or Malignant Cancer Tumors

Breast cancer is the most common form of cancer in women, and invasive ductal carcinoma (IDC) is the most common form of breast cancer. Accurately identifying and categorizing breast cancer subtypes is an important clinical task, and automated methods can be used to save time and reduce error. The goal of this script is to identify IDC when it is present in otherwise unlabeled histopathology images. The dataset consists of approximately five thousand 50x50 pixel RGB digital images of H&E-stained breast histopathology samples that are labeled as either IDC or non-IDC. These numpy arrays are small patches that were extracted from digital images of breast tissue samples. The breast tissue contains many cells but only some of them are cancerous. Patches that are labeled "1" contain cells that are characteristic of invasive ductal carcinoma. For more information about the data, see https://www.ncbi.nlm.nih.gov/pubmed/27563488 and http://spie.org/Publications/Proceedings/Paper/10.1117/12.2043872.

breast cancer detection using KNN and SVM

The objective of the project was to build various models and compare their prediction performance based on accuracy.

An experiment using neural networks to predict obesity-related breast cancer over a small dataset of blood samples.

Detects stage of breast cancer.

Classification of breast cancer diagnosis using Support Vector Machines in Python using Sklearn

This project is a part of research on Breast Cancer Diagnosis with Machine Learning algorithm using data-driven approaches. The final outcomes of the research were later published at an IEEE Conference and added to IEEE Xplore Digital Library.

Breast cancer classification model using machine learning techniques in python

A comparative analysis of best Classification method for Winsconsin Breast Cancer Data.

CNN model for classification of breast histology images "benign" or "malignant" using keras and TensorFlow as backend. Attained a classification accuracy of 85%.

Deep Learning in Medicine Final Project

Classifying Breast Cancer Molecular Subtypes

Breast Cancer Diagnostic - Machine Learning Classification

College project on Breast Cancer Analysis and Prediction using ML

The goal of this project is to classify whether breast cancer is benign or malignant.

Predict Breast Cancer Wisconsin (Diagnostic) using Naive Bayes

The programme written in python uses Machine learning to predict whether the breast cancer is of type Malignant or Benign

This project is to classify breast cancer using SVM-Classifier and further improving the model using GridSearch

Classification of Breast Cancer diagnosis Using Support Vector Machines

In this project, Based on dataset the breast cancer is classified with the help of Support Vector Machine.

Predicting survival outcome in breast cancer patients based on their gene expression