Xception-ResNet Autoencoder for Pneumothorax Segmentation
Abdelbaki Souid, Hédi Sakli
Abstract
Computer vision has made a significant advance in the medical imaging. Pneumothorax is a severe ailment that can be fatal if the patient does not receive proper care. The main way for diagnosing Pneumothorax is by examining the Chest X-ray by a specialist. The urge of experienced radiologists to anticipate whether someone is suffering from pneumothorax or not by examining chest X-rays is indisputable. Such a facility is not available to everyone. Furthermore, in some circumstances, quick diagnosis is required. In this paper We present a deep learning based image segmentation model capable of predicting Pneumothorax cases by localizing it in chest x-ray exam to help the doctor in making this critical choice. Deep Learning has demonstrated its value in multiple domains, outperforming several state-of-the-arts methods. We seek to overcome this challenge by leveraging deep learning capabilities. We used U-Net architecture with Xception as the encoder and ResNet as a decoder. We obtained encouraging findings, and U-Net works exceptionally well in medical imaging. Our work is listed with in as semantic segmentation. With 77.8 (±0.15), our technique obtains a good outcome in terms of Intersection over Union.