Bhattacharyya Debnath, Thirupathi Rao N, Joshua Eali Stephen Neal, Hu Yu-Chen
Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Guntur, 522 502 India.
Department of Computer Science and Engineering, Vignan's Institute of Information Technology (A), Visakhapatnam, 530049 AP India.
Vis Comput. 2022 Sep 8:1-17. doi: 10.1007/s00371-022-02657-1.
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.
肺结节是异常生长物，可能存在病变。双肺都可能出现结节。大多数肺结节是无害的（非癌性/恶性）。肺结节在肺癌中较为罕见。X 光和 CT 扫描可识别肺结节。医生可能将这种生长物称为肺斑、钱币状病变或阴影。为了获得准确的诊断并对肺癌的严重程度有一个良好的评估，对肺部进行适当的计算机断层扫描（CT）是必要的。本研究旨在设计并评估一种深度学习（DL）算法，用于使用 LUNA - 16 数据集识别肺结节（PNs），并使用 DB - Net 检查 PNs 的患病率。该论文指出需要一种新的、资源高效的深度学习架构，并将其命名为 DB - NET。当医生开具 CT 扫描医嘱时，他们需要采用准确且高效的肺结节分割方法，因为他们需要在早期检测出肺癌。然而，由于 CT 图像上结节的特征以及结节隐藏的形状、视觉质量和背景，肺结节的分割是一项艰巨的任务。本文将 DB - NET 模型架构作为一种资源高效的深度学习解决方案，用于应对当前的挑战。此外，它还结合了 Mish 非线性函数和掩码类别权重以提高分割效果。除了包含在 LUNA - 16 测试期间收集的 1200 个肺结节的 LUNA - 16 数据集外，LUNA - 16 数据集还被广泛用于训练和评估所提出的模型。DB - NET 架构在骰子系数指标上比现有的 U - NET 模型高出 88.89%，并且它还达到了与人类专家相似的准确率水平。
