A bi-directional deep learning architecture for lung nodule semantic segmentation

Debnath Bhattacharyya; N Thirupathi Rao; Eali Stephen Neal Joshua; Yu-Chen Hu

doi:10.1007/s00371-022-02657-1

A bi-directional deep learning architecture for lung nodule semantic segmentation

Vis Comput. 2022 Sep 8:1-17. doi: 10.1007/s00371-022-02657-1. Online ahead of print.

Authors

Debnath Bhattacharyya¹, N Thirupathi Rao², Eali Stephen Neal Joshua², Yu-Chen Hu³

Affiliations

¹ Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Guntur, 522 502 India.
² Department of Computer Science and Engineering, Vignan's Institute of Information Technology (A), Visakhapatnam, 530049 AP India.
³ Department of Computer Science and Information Management, Providence University, 200, Sec. 7, Taiwan Boulevard, Shalu Dist., Taichung City, 43301 Taiwan R.O.C.

Abstract

Lung nodules are abnormal growths and lesions may exist. Both lungs may have nodules. Most lung nodules are harmless (not cancerous/malignant). Pulmonary nodules are rare in lung cancer. X-rays and CT scans identify the lung nodules. Doctors may term the growth a lung spot, coin lesion, or shadow. It is necessary to obtain properly computed tomography (CT) scans of the lungs to get an accurate diagnosis and a good estimate of the severity of lung cancer. This study aims to design and evaluate a deep learning (DL) algorithm for identifying pulmonary nodules (PNs) using the LUNA-16 dataset and examine the prevalence of PNs using DB-Net. The paper states that a new, resource-efficient deep learning architecture is called for, and it has been given the name of DB-NET. When a physician orders a CT scan, they need to employ an accurate and efficient lung nodule segmentation method because they need to detect lung cancer at an early stage. However, segmentation of lung nodules is a difficult task because of the nodules' characteristics on the CT image as well as the nodules' concealed shape, visual quality, and context. The DB-NET model architecture is presented as a resource-efficient deep learning solution for handling the challenge at hand in this paper. Furthermore, it incorporates the Mish nonlinearity function and the mask class weights to improve segmentation effectiveness. In addition to the LUNA-16 dataset, which contained 1200 lung nodules collected during the LUNA-16 test, the LUNA-16 dataset was extensively used to train and assess the proposed model. The DB-NET architecture surpasses the existing U-NET model by a dice coefficient index of 88.89%, and it also achieves a similar level of accuracy to that of human experts.

Keywords: Bidirectional feature extraction; Computer-aided diagnosis; Convolutional neural network; Deep learning; Lung cancer.

© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022, Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.