Deep Learning to Improve Breast Cancer Detection on Screening Mammography

Breast Cancer Detection Using Machine Learning

Master's dissertation for breast cancer detection in mammograms using deep learning techniques in Tensorflow. Contains the final report and source code.

1st place solution of RSNA Screening Mammography Breast Cancer Detection competition on Kaggle: https://www.kaggle.com/competitions/rsna-breast-cancer-detection

Breast Cancer Detection classifier built from the The Breast Cancer Histopathological Image Classification (BreakHis) dataset composed of 7,909 microscopic images.

[ICMLSC 2018] On Breast Cancer Detection: An Application of Machine Learning Algorithms on the Wisconsin Diagnostic Dataset

AI app to detect breast cancer in histopathological database

Experiments to show the usage of deep learning to detect breast cancer from breast histopathology images

This project uses mammograms for breast cancer detection using deep learning techniques.

Breast Cancer Detection using ML

This project is made in Matlab Platform and it detects whether a person has cancer or not by taking into account his/her mammogram.

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Using pre-trained model to classify images to detect cancerous cells

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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

SVM (Support Vector Machines) is used to build and train a model using human cell records, and classify cells to predict whether the samples are benign or malignant and display output using Flask Application On Heroku

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A web app for breast cancer detection.

Implementation of SVM Classifier To Perform Classification on the dataset of Breast Cancer Wisconin; to predict if the tumor is cancer or not.

Matlab based GUI to predict breast cancer using Deep Learning

AI Breast cancer detection using InBreast, CBIS-DDSM, MIAS mammography image datasets

An implementation of the L2-SVM for breast cancer detection using the Wisconsin diagnostic dataset.

This is a guided certification project, as a part of Data Science for Social Good initiative

End-to-end breast cancer detection in Python

Code for published article in PLOS ONE. Breast cancer detection in mammograms using DL techniques. Contains source code for data and methods used.

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Breast Cancer Detection App Using Machine Learning XGBoost Classifier

Attempt to detect potential breast cancer from mammograms (MIAS database)

# Breast-cancer-risk-prediction > Necessity, who is the mother of invention. – Plato* ## Welcome to my GitHub repository on Using Predictive Analytics model to diagnose breast cancer. --- ### Objective: The repository is a learning exercise to: * Apply the fundamental concepts of machine learning from an available dataset * Evaluate and interpret my results and justify my interpretation based on observed data set * Create notebooks that serve as computational records and document my thought process. The analysis is divided into four sections, saved in juypter notebooks in this repository 1. Identifying the problem and Data Sources 2. Exploratory Data Analysis 3. Pre-Processing the Data 4. Build model to predict whether breast cell tissue is malignant or Benign ### [Notebook 1](https://github.com/ShiroJean/Breast-cancer-risk-prediction/blob/master/NB1_IdentifyProblem%2BDataClean.ipynb): Identifying the problem and Getting data. **Notebook goal:Identify the types of information contained in our data set** In this notebook I used Python modules to import external data sets for the purpose of getting to know/familiarize myself with the data to get a good grasp of the data and think about how to handle the data in different ways.  ### [Notebook 2](https://github.com/ShiroJean/Breast-cancer-risk-prediction/blob/master/NB2_ExploratoryDataAnalysis.ipynb) Exploratory Data Analysis **Notebook goal:  Explore the variables to assess how they relate to the response variable** In this notebook, I am getting familiar with the data using data exploration and visualization techniques using python libraries (Pandas, matplotlib, seaborn. Familiarity with the data is important which will provide useful knowledge for data pre-processing) ### [Notebook 3](https://github.com/ShiroJean/Breast-cancer-risk-prediction/blob/master/NB3_DataPreprocesing.ipynb) Pre-Processing the data **Notebook goal:Find the most predictive features of the data and filter it so it will enhance the predictive power of the analytics model.** In this notebook I use feature selection to reduce high-dimension data, feature extraction and transformation for dimensionality reduction. This is essential in preparing the data before predictive models are developed. ### [Notebook 4](https://github.com/ShiroJean/Breast-cancer-risk-prediction/blob/master/NB4_PredictiveModelUsingSVM.ipynb) Predictive model using Support Vector Machine (svm) **Notebook goal: Construct predictive models to predict the diagnosis of a breast tumor.** In this notebook, I construct a predictive model using SVM machine learning algorithm to predict the diagnosis of a breast tumor. The diagnosis of a breast tumor is a binary variable (benign or malignant). I also evaluate the model using confusion matrix the receiver operating curves (ROC), which are essential in assessing and interpreting the fitted model. ### [Notebook 5](https://github.com/ShiroJean/Breast-cancer-risk-prediction/blob/master/NB_5%20OptimizingSVMClassifier.ipynb): Optimizing the Support Vector Classifier **Notebook goal: Construct predictive models to predict the diagnosis of a breast tumor.** In this notebook, I aim to tune parameters of the SVM Classification model using scikit-learn.