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

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.

Enhanced Lung Nodule Segmentation using Dung Beetle Optimization based LNS-DualMAGNet Model

“…By employing wavelet pooling in combination with the U-Net++ architecture to extract This method improved the accuracy of segmentation by extracting high-and lowfrequency data from the image. Given that the average IoU is 0.878 and the average dice coefficient is 0.936, the experimental findings on the LIDC-IDRI dataset demonstrated higher performance in contrast to the The LUNA-16 dataset was utilised in the development and evaluation of a deep learning system (DB-NET) for pulmonary nodule recognition by Bhattacharyya, D., et al [20]. In comparison to the present U-NET model, the resource-efficient design known as DB-NET earned a dice coefficient index of 88.89%.…”

“…AUC also shows the likelihood that the model will accurately predict positive or negative samples; a value nearer 1 denotes superior model performance. Equations provide the specifics (18)(19)(20)(21)(22)(23).…”

LACTNet: A Lightweight Real-time Semantic Segmentation Network Based on Aggregation CNN and Transformer

“…Considering the speed requirements for certain application scenarios, an increasing number of real-time semantic segmentation models have emerged [28][29][30][31]. BiAttnNet [32] conceptualized a detail branch that extracted semantic features from target feature mappings.…”

LACTNet: A Lightweight Real-Time Semantic Segmentation Network Based on an Aggregated Convolutional Neural Network and Transformer

“…Considering the speed requirements for certain application scenarios, an increasing number of real-time semantic segmentation models have emerged [37][38][39][40]. BiAttnNet [41] conceptualized a detailed branch that extracted semantic features from target feature mappings.…”