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Enhanced lung image segmentation using deep learning

  • S.I. : Neural Computing for IOT based Intelligent Healthcare Systems
  • Published:
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Abstract

With the advances in technology, assistive medical systems are emerging with rapid growth and helping healthcare professionals. The proactive diagnosis of diseases with artificial intelligence (AI) and its aligned technologies has been an exciting research area in the last decade. Doctors usually detect tuberculosis (TB) by checking the lungs’ X-rays. Classification using deep learning algorithms is successfully able to achieve accuracy almost similar to a doctor in detecting TB. It is found that the probability of detecting TB increases if classification algorithms are implemented on segmented lungs instead of the whole X-ray. The paper’s novelty lies in detailed analysis and discussion of U-Net +  + results and implementation of U-Net +  + in lung segmentation using X-ray. A thorough comparison of U-Net +  + with three other benchmark segmentation architectures and segmentation in diagnosing TB or other pulmonary lung diseases is also made in this paper. To the best of our knowledge, no prior research tried to implement U-Net +  + for lung segmentation. Most of the papers did not even use segmentation before classification, which causes data leakage. Very few used segmentations before classification, but they only used U-Net, which U-Net +  + can easily replace because accuracy and mean_iou of U-Net +  + are greater than U-Net accuracy and mean_iou , discussed in results, which can minimize data leakage. The authors achieved more than 98% lung segmentation accuracy and mean_iou 0.95 using U-Net +  + , and the efficacy of such comparative analysis is validated.

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Data availability

It is confirmed by the authors that data supporting this research finding are present within the article, and the publicly available datasets used in this study are Montgomery County X-ray Set and Shenzhen Hospital X-ray Set [36].

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Authors and Affiliations

  1. Computer Science Engineering Department, Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, 412115, India

    Shilpa Gite, Abhinav Mishra & Ketan Kotecha

  2. Symbiosis Centre for Applied Artificial Intelligence (SCAAI), Symbiosis International (Deemed University), Pune, 412115, India

    Shilpa Gite & Ketan Kotecha

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Gite, S., Mishra, A. & Kotecha, K. Enhanced lung image segmentation using deep learning. Neural Comput & Applic 35, 22839–22853 (2023). https://doi.org/10.1007/s00521-021-06719-8

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