Deep Learning with Lung Segmentation and Bone Shadow Exclusion Techniques for Chest X-Ray Analysis of Lung Cancer

TL;DR

This paper demonstrates that lung segmentation and bone shadow exclusion techniques significantly improve deep learning-based analysis of chest X-rays for lung cancer detection, enhancing accuracy and efficiency.

Contribution

The study introduces effective pre-processing techniques, including lung segmentation and bone shadow exclusion, that improve deep learning performance on chest X-ray analysis for lung cancer detection.

Findings

Bone shadow exclusion improves accuracy in lung X-ray analysis.

Pre-processed datasets without bones yield better deep learning results.

Lung segmentation enhances the detection of suspicious lesions.

Abstract

The recent progress of computing, machine learning, and especially deep learning, for image recognition brings a meaningful effect for automatic detection of various diseases from chest X-ray images (CXRs). Here efficiency of lung segmentation and bone shadow exclusion techniques is demonstrated for analysis of 2D CXRs by deep learning approach to help radiologists identify suspicious lesions and nodules in lung cancer patients. Training and validation was performed on the original JSRT dataset (dataset #01), BSE-JSRT dataset, i.e. the same JSRT dataset, but without clavicle and rib shadows (dataset #02), original JSRT dataset after segmentation (dataset #03), and BSE-JSRT dataset after segmentation (dataset #04). The results demonstrate the high efficiency and usefulness of the considered pre-processing techniques in the simplified configuration even. The pre-processed dataset without bones (dataset #02) demonstrates the much better accuracy and loss results in comparison to the other pre-processed datasets after lung segmentation (datasets #02 and #03).