Data mining approaches to pneumothorax detection: Integrating mask-RCNN and medical transfer learning techniques

Abstract

With the medical condition of pneumothorax, also known as collapsed lung, air builds up in the pleural cavity and causes the lung to collapse. It is a critical disorder that needs to be identified and treated right as it can cause breathing difficulties, low blood oxygen levels, and, in extreme circumstances, death. Chest X-rays are frequently used to diagnose pneumothorax. Using the Mask R-CNN model and medical transfer learning, the proposed work offers•A novel method for pneumothorax segmentation from chest X-rays.•A method that takes advantage of the Mask R-CNN architecture's for object recognition and segmentation.•A modified model to address the issue of segmenting pneumothoraxes and then polish it using a sizable dataset of chest X-rays.The proposed method is tested against other pneumothorax segmentation techniques using a dataset of ‘chest X-rays’ with ‘pneumothorax annotations. The test findings demonstrate that proposed method outperforms other cutting-edge techniques in terms of segmentation accuracy and speed. The proposed method could lead to better patient outcomes by increasing the precision and effectiveness of pneumothorax diagnosis and therapy. Proposed method also benefits other medical imaging activities by using the medical transfer learning approaches which increases the precision of computer-aided diagnosis and treatment planning.