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

arXiv: https://arxiv.org/abs/1712.07632 · HTML: https://mlsanity.com/paper/1712.07632/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Dec 2017.

## Built on by (most cited first, 10 of 10)

- [Detailed Annotations of Chest X-Rays via CT Projection for Report Understanding](https://mlsanity.com/paper/2210.03416/) (2022)
- [AnaXNet: Anatomy Aware Multi-label Finding Classification in Chest X-ray](https://mlsanity.com/paper/2105.09937/) (2021)
- [Chest X-Ray Bone Suppression for Improving Classification of Tuberculosis-Consistent Findings](https://mlsanity.com/paper/2104.04518/) (2021)
- [Mask-Enhanced Segment Anything Model for Tumor Lesion Semantic Segmentation](https://mlsanity.com/paper/2403.05912/) (2024)
- [Self-supervised Model Based on Masked Autoencoders Advance CT Scans Classification](https://mlsanity.com/paper/2210.05073/) (2022)
- [Analyzing Green View Index and Green View Index best path using Google Street View and deep learning](https://mlsanity.com/paper/2104.12627/) (2021)
- [Prediction of COVID-19 using chest X-ray images](https://mlsanity.com/paper/2204.03849/) (2022)
- [Generalizability of Deep Adult Lung Segmentation Models to the Pediatric Population: A Retrospective Study](https://mlsanity.com/paper/2211.02475/) (2022)
- [Treatment classification of posterior capsular opacification (PCO) using automated ground truths](https://mlsanity.com/paper/2211.06114/) (2022)
- [Does image resolution impact chest X-ray based fine-grained Tuberculosis-consistent lesion segmentation?](https://mlsanity.com/paper/2301.04032/) (2023)

## Abstract (arXiv)

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).