Pneumothorax Segmentation: Deep Learning Image Segmentation to predict Pneumothorax
Abstract
Computer vision has shown promising results in medical image processing. Pneumothorax is a deadly condition and if not diagnosed and treated at time then it causes death. It can be diagnosed with chest X-ray images. We need an expert and experienced radiologist to predict whether a person is suffering from pneumothorax or not by looking at the chest X-ray images. Everyone does not have access to such a facility. Moreover, in some cases, we need quick diagnoses. So we propose an image segmentation model to predict and give the output a mask that will assist the doctor in taking this crucial decision. Deep Learning has proved their worth in many areas and outperformed man state-of-the-art models. We want to use the power of these deep learning model to solve this problem. We have used U-net [13] architecture with ResNet [17] as a backbone and achieved promising results. U-net [13] performs very well in medical image processing and semantic segmentation. Our problem falls in the semantic segmentation category.
Cite
@article{arxiv.1912.07329,
title = {Pneumothorax Segmentation: Deep Learning Image Segmentation to predict Pneumothorax},
author = {Karan Jakhar and Avneet Kaur and Meenu Gupta},
journal= {arXiv preprint arXiv:1912.07329},
year = {2024}
}
Comments
Will be updated later on. Somethings need to be updated and corrected