# XNet: A convolutional neural network (CNN) implementation for medical X-Ray image segmentation suitable for small datasets

## TL;DR

XNet is a CNN-based method tailored for medical X-Ray image segmentation that performs well on small datasets, achieving high accuracy and surpassing traditional and existing neural network approaches.

## Contribution

The paper introduces XNet, a CNN architecture optimized for small medical X-Ray datasets, providing robust segmentation with state-of-the-art accuracy.

## Key findings

- Achieves 92% accuracy in X-Ray segmentation
- Outperforms classical image processing techniques
- Improves upon existing neural network methods

## Abstract

X-Ray image enhancement, along with many other medical image processing applications, requires the segmentation of images into bone, soft tissue, and open beam regions. We apply a machine learning approach to this problem, presenting an end-to-end solution which results in robust and efficient inference. Since medical institutions frequently do not have the resources to process and label the large quantity of X-Ray images usually needed for neural network training, we design an end-to-end solution for small datasets, while achieving state-of-the-art results. Our implementation produces an overall accuracy of 92%, F1 score of 0.92, and an AUC of 0.98, surpassing classical image processing techniques, such as clustering and entropy based methods, while improving upon the output of existing neural networks used for segmentation in non-medical contexts. The code used for this project is available online.