SmokerViT: A Transformer-Based Method for Smoker Recognition

Smoking has an economic and environmental impact on society due to the toxic substances it emits.Convolutional Neural Networks(CNNs)need help describing low-level features and can miss important information.Moreover,accurate smoker detection is vital with minimum false alarms.To answer the issue,the researchers of this paper have turned to a self-attention mechanism inspired by the ViT,which has displayed state-of-the-art performance in the classification task.To effectively enforce the smoking prohibition in non-smoking locations,this work presents a Vision Transformer-inspired model called SmokerViT for detecting smokers.Moreover,this research utilizes a locally curated dataset of 1120 images evenly distributed among the two classes(Smoking and NotSmoking).Further,this research performs augmentations on the smoker detection dataset to have many images with various representations to overcome the dataset size limitation.Unlike convolutional operations used in most existing works,the proposed SmokerViT model employs a self-attention mechanism in the Transformer block,making it suitable for the smoker classification problem.Besides,this work integrates the multi-layer perceptron head block in the SmokerViT model,which contains dense layers with rectified linear activation and linear kernel regularizer with L2 for the recognition task.This work presents an exhaustive analysis to prove the efficiency of the proposed SmokerViT model.The performance of the proposed SmokerViT performance is evaluated and compared with the existing methods,where it achieves an overall classification accuracy of 97.77%,with 98.21%recall and 97.35%precision,outperforming the state-of-the-art deep learning models,including convolutional neural networks(CNNs)and other vision transformer-based models.

Simultaneous Bilateral Thoracoscopic Pneumonectomy for Early Multiple Primary Lung Cancer Feasibility Analysis

Objective:To analyze the feasibility of simultaneous bilateral thoracoscopic lung resection in the treatment of multiple primary lung cancers in the early stage.Methods:The study time range is between March 2019 and March 2021.A sample of 30 patients with early multiple primary lung cancer admitted to this hospital were included,and they were divided into a study group,a control group,and samples within the group using a random number table scheme n=15,patients in the control group underwent staged bilateral thoracoscopic pneumonectomy,and patients in the study group underwent bilateral thoracoscopic pneumonectomy at the same time.The indicators of the two groups were compared and analyzed.Results:There was no significant difference in the operation time and intraoperative blood loss between the two groups(P>0.05).There were significant differences in the VAS score,total length of hospital stay,and total surgical costs on the first day after surgery(P<0.05);there was no significant difference in the two groups'postoperative recovery indicators and the incidence of complications(P>0.05).Conclusion:It is safe and feasible to treat patients with multiple primary lung cancer in both lungs at the same time with simultaneous bilateral thoracoscopic surgery,and is suitable for promotion.