Two-Stage Convolutional Neural Network for Breast Cancer Histology Image Classification. ICIAR 2018 Grand Challenge on BreAst Cancer Histology images (BACH)
This project aims to predict people who will survive breast cancer using machine learning models with the help of clinical data and gene expression profiles of the patients.
Microwave Radar-based Imaging Toolbox (MERIT) is free and open-source software for microwave radar-basaed imaging. Including getting started guides and example data, MERIT is a flexible and extensible framework for developing, testing, running and optimising radar-based imaging algorithms.
Breast Cancer Detection classifier built from the The Breast Cancer Histopathological Image Classification (BreakHis) dataset composed of 7,909 microscopic images.
OncoText is an information extraction service for breast pathology reports. It supports over 20 categories including DCIS, includes pretrained models, and supports flexible addition of new categories, new training data, and parsing new reports.
A menu based multiple chronic disease detection system which will detect if a person is suffering from a severe disease by taking an essential input image.
This is a Machine Learning web app developed using Python and StreamLit. Uses algorithms like Logistic Regression, KNN, SVM, Random Forest, Gradient Boosting, and XGBoost to build powerful and accurate models to predict the status of the user (High Risk / Low Risk) with respect to Heart Attack and Breast Cancer.
The Invasive Ductal Carcinoma (IDC) Detection System is an open source computer vision program created to classify IDC positive and negative samples. This project is a complete system including a locally hosted webserver / UI / API allowing you to manage your pipeline.
Flask based Web app with 5 Machine Learning Models including 10 most common Disease prediction and Coronavirus prediction with their symptoms as inputs and Breast cancer , Chronic Kidney Disease and Heart Disease predictions with their Medical report as inputs