twishackaul / gastrointestinal-bleeding-classifier

This classifier model uses the dataset of multiple images of gi bleeding and without gi bleeding and then train itself to get tested on a given test data.

Auto-WCEBleedGen Challenge Readme

• The aim of this project involved testing and evaluation of Artificial Intelligence (AI) models for automatic detection and classification of bleeding and non-bleeding frames extracted from Wireless Capsule Endoscopy (WCE) videos.

• The training dataset consists of 2618 bleeding and non-bleeding WCE frames collected from multiple internet resources, datasets with a vast variety and types of gastrointestinal (GI) bleeding throughout the GI tract along with medically validated binary masks and bounding boxes in three formats (txt, XML and YOLO txt).

• The training dataset is passed through a deep learning architecture based Convolutional neural network(CNN) for feature extraction and then the feature map is passed to the Rectified Linear Unit (ReLU) activation function to get the resulting classification into two classes - GI bleeding and No GI bleeding.

• The test dataset is an independently collected WCE data containing bleeding and non-bleeding frames of more than 30 patients suffering from acute, chronic and occult GI bleeding referred at Department of Gastroenterology and HNU, All India Institute of Medical Sciences, New Delhi, India.

• This project is done by team 4 Geeks from IGDTUW.