Found 548 repositories(showing 30)
mistersharmaa
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
oreHGA
Machine Learning algorithms that predict whether a breast cancer tumor is Malignant or Benign
beingdatum
No description available
Serenayg
Classification models for breast cancer dataset for Deployment
pik1989
No description available
No description available
yekaditya15
No description available
faramarzzareian
No description available
sayandhsr
No description available
Pragati2793
No description available
neilshah13
Analysis and prediction on breast cancer, with a focus on Singapore. This utilises various datasets and models aimed to provide as comprehensive a coverage as possible.
End to
thillairam007
No description available
Nithish2312
Based on XGBoost Algorithm which provides 98.25% accuracy.
Sabiha570
No description available
afiadata
A simple classification model for breast cancer using python
naimam
Breast cancer prediction using popular classification algorithms & image detection.
ShridattaP20
No description available
rabebkaabi
No description available
Shubham3023
No description available
vatsalrrana
No description available
SamayJain10
No description available
Uni-Creator
Breast Cancer Prediction App uses ML models to classify tumors as benign or malignant based on 30 medical features. Users can input values or autofill a random row. The app displays predictions from multiple models with confidence scores and a comparison chart. Built with Streamlit and trained on real-world breast cancer data.
Mainul8
Breast cancer prediction website build to predict whether patient is suffering from breast cancer benign or malignant
tejashwini80
Machine Learning Project to predict Breast Cancer
prajakta-3-b
No description available
Sicakyuz
No description available
PerfectPlanB
To identify breast cancer using various machine learning algorithms. • Predicting if the cancer diagnosis is benign or malignant based on several observations/features • 30 features are used, examples: radius (mean of distances from center to points on the perimeter) texture (standard deviation of gray-scale values) perimeter area smoothness (local variation in radius lengths) compactness (perimeter^2 / area - 1.0) concavity (severity of concave portions of the contour) concave points (number of concave portions of the contour) symmetry fractal dimension ("coastline approximation" - 1) • Datasets are linearly separable using all 30 input features • Number of Instances: 569 • Class Distribution: 212 Malignant, 357 Benign • Target class: Malignant Benign
sheenakhanna
No description available
Suru1496
No description available