An Enhanced Lung Cancer Detection Approach Using Dual-Model Deep Learning Technique

Lung cancer continues to be a leading cause of cancer-related deaths worldwide,emphasizing the critical need for improved diagnostic techniques.Early detection of lung tumors significantly increases the chances of successful treatment and survival.However,current diagnostic methods often fail to detect tumors at an early stage or to accurately pinpoint their location within the lung tissue.Single-model deep learning technologies for lung cancer detection,while beneficial,cannot capture the full range of features present in medical imaging data,leading to incomplete or inaccurate detection.Furthermore,it may not be robust enough to handle the wide variability in medical images due to different imaging conditions,patient anatomy,and tumor characteristics.To overcome these disadvantages,dual-model or multi-model approaches can be employed.This research focuses on enhancing the detection of lung cancer by utilizing a combination of two learning models:a Convolutional Neural Network(CNN)for categorization and the You Only Look Once(YOLOv8)architecture for real-time identification and pinpointing of tumors.CNNs automatically learn to extract hierarchical features from raw image data,capturing patterns such as edges,textures,and complex structures that are crucial for identifying lung cancer.YOLOv8 incorporates multiscale feature extraction,enabling the detection of tumors of varying sizes and scales within a single image.This is particularly beneficial for identifying small or irregularly shaped tumors that may be challenging to detect.Furthermore,through the utilization of cutting-edge data augmentation methods,such as Deep Convolutional Generative Adversarial Networks(DCGAN),the suggested approach can handle the issue of limited data and boost the models’ability to learn from diverse and comprehensive datasets.The combined method not only improved accuracy and localization but also ensured efficient real-time processing,which is crucial for practical clinical applications.The CNN achieved an accuracy of 97.67%in classifying lung tissues into healthy and cancerous categories.The YOLOv8 model achieved an Intersection over Union(IoU)score of 0.85 for tumor localization,reflecting high precision in detecting and marking tumor boundaries within the images.Finally,the incorporation of synthetic images generated by DCGAN led to a 10%improvement in both the CNN classification accuracy and YOLOv8 detection performance.

MARIE:One-Stage Object Detection Mechanism for Real-Time Identifying of Firearms

Security and safety remain paramount concerns for both governments and individuals worldwide.In today’s context,the frequency of crimes and terrorist attacks is alarmingly increasing,becoming increasingly intolerable to society.Consequently,there is a pressing need for swift identification of potential threats to preemptively alert law enforcement and security forces,thereby preventing potential attacks or violent incidents.Recent advancements in big data analytics and deep learning have significantly enhanced the capabilities of computer vision in object detection,particularly in identifying firearms.This paper introduces a novel automatic firearm detection surveillance system,utilizing a one-stage detection approach named MARIE(Mechanism for Realtime Identification of Firearms).MARIE incorporates the Single Shot Multibox Detector(SSD)model,which has been specifically optimized to balance the speed-accuracy trade-off critical in firearm detection applications.The SSD model was further refined by integrating MobileNetV2 and InceptionV2 architectures for superior feature extraction capabilities.The experimental results demonstrate that this modified SSD configuration provides highly satisfactory performance,surpassing existing methods trained on the same dataset in terms of the critical speedaccuracy trade-off.Through these innovations,MARIE sets a new standard in surveillance technology,offering a robust solution to enhance public safety effectively.

Advancements in Liver Tumor Detection:A Comprehensive Review of Various Deep Learning Models

Liver cancer remains a leading cause of mortality worldwide,and precise diagnostic tools are essential for effective treatment planning.Liver Tumors(LTs)vary significantly in size,shape,and location,and can present with tissues of similar intensities,making automatically segmenting and classifying LTs from abdominal tomography images crucial and challenging.This review examines recent advancements in Liver Segmentation(LS)and Tumor Segmentation(TS)algorithms,highlighting their strengths and limitations regarding precision,automation,and resilience.Performance metrics are utilized to assess key detection algorithms and analytical methods,emphasizing their effectiveness and relevance in clinical contexts.The review also addresses ongoing challenges in liver tumor segmentation and identification,such as managing high variability in patient data and ensuring robustness across different imaging conditions.It suggests directions for future research,with insights into technological advancements that can enhance surgical planning and diagnostic accuracy by comparing popular methods.This paper contributes to a comprehensive understanding of current liver tumor detection techniques,provides a roadmap for future innovations,and improves diagnostic and therapeutic outcomes for liver cancer by integrating recent progress with remaining challenges.

基于改进Detection Transformer的棉花幼苗与杂草检测模型研究

基于深度学习的目标检测技术在棉花幼苗与杂草检测领域已取得一定进展。论文提出了基于改进Detection Transformer的棉花幼苗与杂草检测模型,以提高杂草目标检测的准确率和效率。首先,引入了可变形注意力模块替代原始模型中的Transformer注意力模块,提高模型对特征图目标形变的处理能力。提出新的降噪训练机制,解决了二分图匹配不稳定问题。提出混合查询选择策略,提高解码器对目标类别和位置信息的利用效率。使用Swin Transformer作为网络主干,提高模型特征提取能力。通过对比原网络,论文提出的模型方法在训练过程中表现出更快的收敛速度,并且在准确率方面提高了6.7%。

Ultralow detection limit of giant magnetoresistance biosensor using Fe3O4–graphene composite nanoparticle label

A special Fe3O4nanoparticles–graphene（Fe3O4–GN） composite as a magnetic label was employed for biodetection using giant magnetoresistance（GMR） sensors with a Wheatstone bridge. The Fe3O4–GN composite exhibits a strong ferromagnetic behavior with the saturation magnetization MS of approximately 48 emu/g, coercivity HC of 200 Oe, and remanence Mr of 8.3 emu/g, leading to a large magnetic fringing field. However, the Fe3O4 nanoparticles do not aggregate together, which can be attributed to the pinning and separating effects of graphene sheet to the magnetic particles. The Fe3O4–GN composite is especially suitable for biodetection as a promising magnetic label since it combines two advantages of large fringing field and no aggregation. As a result, the concentration x dependence of voltage difference ｜？V｜ between detecting and reference sensors undergoes the relationship of ｜？V｜ = 240.5 lgx ＋ 515.2 with an ultralow detection limit of 10 ng/mL（very close to the calculated limit of 7 ng/mL） and a wide detection range of 4 orders.

Estimation of multivariate detection limits of four quality parameters in licorice using MEMS–NIR spectrometry coupled with two sampling accessories

In this work,multivariate detection limits(MDL)estimator was obtained based on the microelectro-mechanical systems–near infrared(MEMS–NIR)technology coupled with two sampling accessories to assess the detection capability of four quality parameters(glycyrrhizic acid,liquiritin,liquiritigenin and isoliquiritin)in licorice from di®erent geographical regions.112 licorice samples were divided into two parts(calibration set and prediction set)using Kennard–Stone(KS)method.Four quality parameters were measured using high-performance liquid chromatography(HPLC)method according to Chinese pharmacopoeia and previous studies.The MEMS–NIR spectra were acquired from¯ber optic probe(FOP)and integrating sphere,then the partial least squares(PLS)model was obtained using the optimum processing method.Chemometrics indicators have been utilized to assess the PLS model performance.Model assessment using chemometrics indicators is based on relative mean prediction error of all concentration levels,which indicated relatively low sensitivity for low-content analytes(below 1000 parts per million(ppm)).Therefore,MDL estimator was introduced with alpha error and beta error based on good prediction characteristic of low concentration levels.The result suggested that MEMS–NIR technology coupled with fiber optic probe(FOP)and integrating sphere was able to detect minor analytes.The result further demonstrated that integrating sphere mode(i.e.,MDL0:05;0:05,0.22%)was more robust than FOP mode(i.e.,MDL0:05;0:05,0.48%).In conclusion,this research proposed that MDL method was helpful to determine the detection capabilities of low-content analytes using MEMS–NIR technology and successful to compare two sampling accessories.

DISCUSSION ON DECISION LIMIT AND DETECTION LIMIT IN SPECTROSCOPY

Based on fundamental arguments, the expressions for the decision limit and the detection limit both in the count domain and in the count rate domain are derived.These expressions are found to be different from those shown in the existing literature.

Theoretical and experimental assessment of a novel method to establish the complete measurement range of the calorimeter and its limit of detection and quantification

For determining the accuracy of a calorimeter over the instrument’s entire measuring range,a novel method has been established.For this new approach,(a)benzoic acid(C_(6)H_(5)CO_(2)H) as a certified reference material(CRM),(b)SiO_(2) and(c)a mixture of CRM benzoic acid and SiO_(2) have been used.To illustrate the essential difference between 1)the novel analytical method for control of the entire measurement range and 2)the calorimeter calibration,both applications of benzoic acid(BA)have been demonstrated.An experimental result showed that BA was successfully used to check the whole calorimeter measurement range.The results also showed that the same new method was successfully applied to determine the limit of detection and quantification.A new instrument testing process and a new measurement technique have thus been established.In this way,the cost of using CRM to control the accuracy of measuring the entire measuring range of the calorimeter,as shown in this paper,is minimized.The requirements of the ISO/IEC 17025:2017 standard are satisfied.ISO/IEC 17025:2017,together with ISO 9001:2015(quality management systems),ISO 14001:2015(relate to environmental protection)and ISO45001:2018(occupational safety),constitute an integrated quality system by which a testing laboratory may also accredit.

Achieving Ultra-Low Detection Limit Using Nanofiber Labels for Rapid Disease Detection

Early diagnosis of diseases is critical in its effective management. Traditional disease detection methods require specialized equipment and trained personnel. With the introduction of rapid diagnostic test kits (RDTs), disease detection has become easier and faster. However, these RDTs have failed to compete with the specialized laboratory equipment due to their high detection limits and false alarm rates. This paper presents a novel method of using carbon nanofibers (CNFs) grown on glass microballoons (NMBs) to achieve ultra-low detection limits in RDTs. The NMBs have millions of nanosized CNFs grown on each microballoon, with each CNF having a strong bonding affinity for antibodies. The NMBs conjugated with secondary antibodies have therefore a significantly higher probability of capturing minute antigen concentrations in solution. Furthermore, the dark color formation at the capture zone makes visual disease detection possible. Human Immunoglobulin G (IgG) was selected as the model analyte to study the performance of NMBs using a sandwich immunoassay protocol. Ultra-low electrical detection limit of (4 pg/ml) and rapid re- sponse (~1 minute) was achieved using this method.

Analysis of detection limit to time-resolved coherent anti-Stokes Raman scattering nanoscopy

In the implementation of CARS nanoscopy, signal strength decreases with focal volume size decreasing. A crucial problem that remains to be solved is whether the reduced signal generated in the suppressed focal volume can be detected. Here reported is a theoretical analysis of detection limit （DL） to time-resolved CARS （T-CARS） nanoscopy based on our proposed additional probe-beam-induced phonon depletion （APIPD） method for the low concentration samples. In order to acquire a detailed shot-noise limited signal-to-noise （SNR） and the involved parameters to evaluate DL, the T-CARS process is described with full quantum theory to estimate the extreme power density levels of the pump and Stokes beams determined by saturation behavior of coherent phonons, which are both actually on the order of ~ 109 W/cm2. When the pump and Stokes intensities reach such values and the total intensity of the excitation beams arrives at a maximum tolerable by most biological samples in a certain suppressed focal volume （40-nm suppressed focal scale in APIPD method）, the DL correspondingly varies with exposure time, for example, DL values are 103 and 102 when exposure times are 20 ms and 200 ms respectively.

A New Method for Decreasing Mass Limit of Detection and Increasing Number of Theoretical Plates in Capillary Electrophoresis with Amperometric Detection

A new method was developed to decrease the mass limit of detection (LOD) and increase the number of theoretical plates (N) in capillary electrophoresis with amperometric detection. When the single microcylinder electrode, the 10 um ID capillary with the etched detection end and the in-capillary alignment were used, the mass LOD for phenol was reduced 124 times and N was increased 36 times in comparison with the normal situation.

Improvement of High-Speed Detection Algorithm for Nonwoven Material Defects Based on Machine Vision

Defect detection is vital in the nonwoven material industry,ensuring surface quality before producing finished products.Recently,deep learning and computer vision advancements have revolutionized defect detection,making it a widely adopted approach in various industrial fields.This paper mainly studied the defect detection method for nonwoven materials based on the improved Nano Det-Plus model.Using the constructed samples of defects in nonwoven materials as the research objects,transfer learning experiments were conducted based on the Nano DetPlus object detection framework.Within this framework,the Backbone,path aggregation feature pyramid network(PAFPN)and Head network models were compared and trained through a process of freezing,with the ultimate aim of bolstering the model's feature extraction abilities and elevating detection accuracy.The half-precision quantization method was used to optimize the model after transfer learning experiments,reducing model weights and computational complexity to improve the detection speed.Performance comparisons were conducted between the improved model and the original Nano Det-Plus model,YOLO,SSD and other common industrial defect detection algorithms,validating that the improved methods based on transfer learning and semi-precision quantization enabled the model to meet the practical requirements of industrial production.

A Hybrid Intrusion Detection Method Based on Convolutional Neural Network and AdaBoost

To solve the problem of poor detection and limited application range of current intrusion detection methods,this paper attempts to use deep learning neural network technology to study a new type of intrusion detection method.Hence,we proposed an intrusion detection algorithm based on convolutional neural network(CNN)and AdaBoost algorithm.This algorithm uses CNN to extract the characteristics of network traffic data,which is particularly suitable for the analysis of continuous and classified attack data.The AdaBoost algorithm is used to classify network attack data that improved the detection effect of unbalanced data classification.We adopt the UNSW-NB15 dataset to test of this algorithm in the PyCharm environment.The results show that the detection rate of algorithm is99.27%and the false positive rate is lower than 0.98%.Comparative analysis shows that this algorithm has advantages over existing methods in terms of detection rate and false positive rate for small proportion of attack data.

Investigation of the factors affecting the limit of detection of laser-induced breakdown spectroscopy for surface inspection

Laser-induced breakdown spectroscopy(LIBS) was examined to detect a trace substance adhered onto Al alloys for the surface inspection of materials to be adhesively bonded. As an example of Si contamination, silicone oil was employed and sprayed onto substrates with a controlled surface concentration. LIBS measurements employing nanosecond UV pulses(λ?=?266 nm) and an off-axis emission collection system with different detecting heights were performed. Because surface contaminants are involved in the plasma formed by laser ablation of the substrates, the relative contribution of the surface contaminants and the substrates to the plasma emission could be changed depending on the conditions for plasma formation. The limit of detection(LOD) was evaluated under several detecting conditions for investigating the factors that affected the LOD. A significant factor was the standard deviation values of signal intensities obtained for the clean substrates. This value varied depending on the measurement conditions.For the Al alloy(A6061), the smallest LOD obtained was 0.529 μg?·?cm^(-2). Furthermore, an improved LOD(0.299 μg?·?cm^(-2)) was obtained for the Al alloy with a lower Si content.

IDS-INT:Intrusion detection system using transformer-based transfer learning for imbalanced network traffic

A network intrusion detection system is critical for cyber security against llegitimate attacks.In terms of feature perspectives,network traffic may include a variety of elements such as attack reference,attack type,a subcategory of attack,host information,malicious scripts,etc.In terms of network perspectives,network traffic may contain an imbalanced number of harmful attacks when compared to normal traffic.It is challenging to identify a specific attack due to complex features and data imbalance issues.To address these issues,this paper proposes an Intrusion Detection System using transformer-based transfer learning for Imbalanced Network Traffic(IDS-INT).IDS-INT uses transformer-based transfer learning to learn feature interactions in both network feature representation and imbalanced data.First,detailed information about each type of attack is gathered from network interaction descriptions,which include network nodes,attack type,reference,host information,etc.Second,the transformer-based transfer learning approach is developed to learn detailed feature representation using their semantic anchors.Third,the Synthetic Minority Oversampling Technique(SMOTE)is implemented to balance abnormal traffic and detect minority attacks.Fourth,the Convolution Neural Network(CNN)model is designed to extract deep features from the balanced network traffic.Finally,the hybrid approach of the CNN-Long Short-Term Memory(CNN-LSTM)model is developed to detect different types of attacks from the deep features.Detailed experiments are conducted to test the proposed approach using three standard datasets,i.e.,UNsWNB15,CIC-IDS2017,and NSL-KDD.An explainable AI approach is implemented to interpret the proposed method and develop a trustable model.

Automated Vulnerability Detection of Blockchain Smart Contacts Based on BERT Artificial Intelligent Model

The widespread adoption of blockchain technology has led to the exploration of its numerous applications in various fields.Cryptographic algorithms and smart contracts are critical components of blockchain security.Despite the benefits of virtual currency,vulnerabilities in smart contracts have resulted in substantial losses to users.While researchers have identified these vulnerabilities and developed tools for detecting them,the accuracy of these tools is still far from satisfactory,with high false positive and false negative rates.In this paper,we propose a new method for detecting vulnerabilities in smart contracts using the BERT pre-training model,which can quickly and effectively process and detect smart contracts.More specifically,we preprocess and make symbol substitution in the contract,which can make the pre-training model better obtain contract features.We evaluate our method on four datasets and compare its performance with other deep learning models and vulnerability detection tools,demonstrating its superior accuracy.

Esophageal cancer screening,early detection and treatment:Current insights and future directions

Esophageal cancer ranks among the most prevalent malignant tumors globally,primarily due to its highly aggressive nature and poor survival rates.According to the 2020 global cancer statistics,there were approximately 604000 new cases of esophageal cancer,resulting in 544000 deaths.The 5-year survival rate hovers around a mere 15%-25%.Notably,distinct variations exist in the risk factors associated with the two primary histological types,influencing their worldwide incidence and distribution.Squamous cell carcinoma displays a high incidence in specific regions,such as certain areas in China,where it meets the cost-effect-iveness criteria for widespread endoscopy-based early diagnosis within the local population.Conversely,adenocarcinoma(EAC)represents the most common histological subtype of esophageal cancer in Europe and the United States.The role of early diagnosis in cases of EAC originating from Barrett's esophagus(BE)remains a subject of controversy.The effectiveness of early detection for EAC,particularly those arising from BE,continues to be a debated topic.The variations in how early-stage esophageal carcinoma is treated in different regions are largely due to the differing rates of early-stage cancer diagnoses.In areas with higher incidences,such as China and Japan,early diagnosis is more common,which has led to the advancement of endoscopic methods as definitive treatments.These techniques have demonstrated remarkable efficacy with minimal complications while preserving esophageal functionality.Early screening,prompt diagnosis,and timely treatment are key strategies that can significantly lower both the occurrence and death rates associated with esophageal cancer.

Feature extraction for machine learning-based intrusion detection in IoT networks

A large number of network security breaches in IoT networks have demonstrated the unreliability of current Network Intrusion Detection Systems(NIDSs).Consequently,network interruptions and loss of sensitive data have occurred,which led to an active research area for improving NIDS technologies.In an analysis of related works,it was observed that most researchers aim to obtain better classification results by using a set of untried combinations of Feature Reduction(FR)and Machine Learning(ML)techniques on NIDS datasets.However,these datasets are different in feature sets,attack types,and network design.Therefore,this paper aims to discover whether these techniques can be generalised across various datasets.Six ML models are utilised:a Deep Feed Forward(DFF),Convolutional Neural Network(CNN),Recurrent Neural Network(RNN),Decision Tree(DT),Logistic Regression(LR),and Naive Bayes(NB).The accuracy of three Feature Extraction(FE)algorithms is detected;Principal Component Analysis(PCA),Auto-encoder(AE),and Linear Discriminant Analysis(LDA),are evaluated using three benchmark datasets:UNSW-NB15,ToN-IoT and CSE-CIC-IDS2018.Although PCA and AE algorithms have been widely used,the determination of their optimal number of extracted dimensions has been overlooked.The results indicate that no clear FE method or ML model can achieve the best scores for all datasets.The optimal number of extracted dimensions has been identified for each dataset,and LDA degrades the performance of the ML models on two datasets.The variance is used to analyse the extracted dimensions of LDA and PCA.Finally,this paper concludes that the choice of datasets significantly alters the performance of the applied techniques.We believe that a universal(benchmark)feature set is needed to facilitate further advancement and progress of research in this field.

YOLO-DD:Improved YOLOv5 for Defect Detection

As computer technology continues to advance,factories have increasingly higher demands for detecting defects.However,detecting defects in a plant environment remains a challenging task due to the presence of complex backgrounds and defects of varying shapes and sizes.To address this issue,this paper proposes YOLO-DD,a defect detectionmodel based on YOLOv5 that is effective and robust.To improve the feature extraction process and better capture global information,the vanilla YOLOv5 is augmented with a new module called Relative-Distance-Aware Transformer(RDAT).Additionally,an Information Gap Filling Strategy(IGFS)is proposed to improve the fusion of features at different scales.The classic lightweight attention mechanism Squeeze-and-Excitation(SE)module is also incorporated into the neck section to enhance feature expression and improve the model’s performance.Experimental results on the NEU-DET dataset demonstrate that YOLO-DDachieves competitive results compared to state-of-the-art methods,with a 2.0% increase in accuracy compared to the original YOLOv5,achieving 82.41% accuracy and38.25FPS(framesper second).Themodel is also testedon a self-constructed fabric defect dataset,and the results show that YOLO-DD is more stable and has higher accuracy than the original YOLOv5,demonstrating its stability and generalization ability.The high efficiency of YOLO-DD enables it to meet the requirements of industrial high accuracy and real-time detection.

Overview of radar detection methods for low altitude targets in marine environments

In this paper,a comprehensive overview of radar detection methods for low-altitude targets in maritime environments is presented,focusing on the challenges posed by sea clutter and multipath scattering.The performance of the radar detection methods under sea clutter,multipath,and combined conditions is categorized and summarized,and future research directions are outlined to enhance radar detection performance for low-altitude targets in maritime environments.