Segmentation Performance Analysis of Transfer Learning Models on X-Ray Pneumonia Images

Abstract

Segmentation of pneumonia areas on chest X-rays is essential to improve the accuracy of recognition tasks and subsequent diagnosis. The capabilities of deep learning techniques, U-Net, SegNet, and DeepLabV3, are assessed to achieve these purposes. Using transfer learning, these models were adapted to pneumonia-specific datasets. The evaluation focuses on Intersection over Union (IoU) and accuracy metrics. Results show that DeepLabV3 outperforms U-Net and SegNet, achieving 84.4% accuracy and 81% IoU. U-Net achieves 80.3% accuracy and 68% IoU, while SegNet achieves 81.0% accuracy and 70% IoU. These findings highlight the potential of transfer learning models to automate the segmentation of pneumonia-affected regions, thereby facilitating timely and accurate medical intervention.