Kaggle link: https://www.kaggle.com/c/rsna-pneumonia-detection-challenge
Author: Min Zhou (minzhou@bu.edu), Andrew Stoycos (astoycos@bu.edu)
Project progress: Trello
working
is the folder including a Jupyter Notebook for analyzing and processing our dataset.app
folder is the web application of our product.yolo_model
folder contains some model config files and a Jupyter Notebook used to train the model.requirements.txt
contains some necessary python libraries to run our code.MASKrcnn_model
contains the python code for Mask-RCNN model.Lung_Segmentation
contains python code for segmenting lungs before training the Mask-RCNN model. Doctors, patients and medical professionals, need a product to help them to improve the efficiency and reach of diagnostic services.
This is an AI (machine learning/deep learning) model which can automatically detect a signal of pneumonia in medical images.
Current pneumonia diagnosis must be completed by a medical professional following a chest X-Ray and physical exam
Our product will automate initial detection (imaging screening) of potential pneumonia cases and create bounding boxes around the areas of interest in order to prioritize and expedite their review.
YOLO is an open source real-time object detection model. It has 106 layers and it's using localization, classificaiton, regression and Focal loss. The benifits of using YOLO v3 are listing below:
git clone https://github.com/minzhou1003/ec601-project.git
cd ec601-project
virtualenv --python python3 env
source env/bin/activate
pip install -r requirements.txt
Download the dataset in the same directory of this project. You should get a folder called input
.
Go to the working directory and open your jupyter notebook:
cd working
jupyter notebook
See our app instruction.