Pneumothorax Segmentation: Deep Learning Image Segmentation to predict Pneumothorax

TL;DR

This paper presents a deep learning-based image segmentation model using U-net with ResNet backbone to assist in rapid and accurate pneumothorax diagnosis from chest X-ray images, addressing limited access to expert radiologists.

Contribution

It introduces a novel application of U-net with ResNet backbone for pneumothorax segmentation, improving diagnostic support in medical imaging.

Findings

Achieved promising segmentation accuracy

Demonstrated effectiveness of U-net with ResNet in medical images

Potential to assist doctors in quick diagnosis

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.