Breast-Cancer-Detection

Overview

Among many cancers, breast cancer is the second most common cause of death in women. Early detection and early treatment reduce breast cancer mortality. Mammography plays an important role in breast cancer screening because it can detect early breast masses or calcification regions.

Signs of Breast Cancer:

1. Weight loss

2. Skin changes

3. Pain

4. Breast changes

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

For the diagnosis of breast cancer doctors often use additional tests to find or diagnose breast cancer. A mammogram is an X-ray picture of the breast. Doctors use a mammogram to look for early signs of breast cancer. Regular mammograms are the best tests doctors have to find breast cancer early, sometimes up to three years before it can be felt. A mammogram shows how dense the breasts are. Women with dense breasts have a higher risk of getting breast cancer.

Dataset

The dataset contains breast mammography images(224,224,3). With labels of:

1. Density

The levels of density are: A: (1) Almost entirely fatty indicates that the breasts are almost entirely composed of fat. About 1 in 10 women has this result. B: (2) Scattered areas of fibroglandular density indicates there are some scattered areas of density, but the majority of the breast tissue is nondense. About 4 in 10 women have this result. C: (3) Heterogeneously dense indicates that there are some areas of nondense tissue, but that the majority of the breast tissue is dense. About 4 in 10 women have this result. D: (4) Extremely dense indicates that nearly all of the breast tissue is dense. About 1 in 10 women has this result.

2. Tumour

A: Benign (noncancerous) B: Malignant (cancerous)

Training Images for each Class

Benign

Malignant

Image Processing

Since the mammograms looks blury and dull, image preprocessing has been done to increase the sharpness and contrast of the image.

Why Deep-Learning?

AI system studying X-ray mammograms was shown to be better than human experts when it came to predicting whether or not a patient has breast cancer. More specifically, the model was found to be as good as two doctors looking at the images, and better at spotting cancer than a single doctor, while also reducing the number of “false-negative” results. Such systems will never replace medical staff, but would serve as an extra set of eyes, while also being able to work 24/7 without getting tired or making mistakes.

Model Deployment using Gradio

!git clone https://github.com/Puneetpotter/Breast-Cancer-Detection.git

%cd Breast-Cancer-Detection/

pip install -r requirements.txt

!python app.py

Open the link to use the app

App Display

1. Web Page

2. Prediction

3. Prediction after cropping image (in the app itself)