Detection Framework Research Articles

Multi-scale Lesion Feature Fusion and Location-Aware for Chest Multi-disease Detection.

Accurately identifying and locating lesions in chest X-rays has the potential to significantly enhance diagnostic efficiency, quality, and interpretability. However, current methods primarily focus on detecting of specific diseases in chest X-rays, disregarding the presence of multiple diseases in a single chest X-ray scan. Moreover, the diversity in lesion locations and attributes introduces complexity in accurately discerning specific traits for each lesion, leading to diminished accuracy when detecting multiple diseases. To address these issues, we propose a novel detection framework that enhances multi-scale lesion feature extraction and fusion, improving lesion position perception and subsequently boosting chest multi-disease detection performance. Initially, we construct a multi-scale lesion feature extraction network to tackle the uniqueness of various lesion features and locations, strengthening the global semantic correlation between lesion features and their positions. Following this, we introduce an instance-aware semantic enhancement network that dynamically amalgamates instance-specific features with high-level semantic representations across various scales. This adaptive integration effectively mitigates the loss of detailed information within lesion regions. Additionally, we perform lesion region feature mapping using candidate boxes to preserve crucial positional information, enhancing the accuracy of chest disease detection across multiple scales. Experimental results on the VinDr-CXR dataset reveal a 6% increment in mean average precision (mAP) and an 8.4% improvement in mean recall (mR) when compared to state-of-the-art baselines. This demonstrates the effectiveness of the model in accurately detecting multiple chest diseases by capturing specific features and location information.

A Novel Detection Framework via Drift Compensation for Inter-Board Differences

A Novel Detection Framework via Drift Compensation for Inter-Board Differences

Machine Learning Framework for Early Detection of Crop Disease

Machine Learning Framework for Early Detection of Crop Disease

Enhancing Emergency Vehicle Detection: A Deep Learning Approach with Multimodal Fusion

Emergency vehicle detection plays a critical role in ensuring timely responses and reducing accidents in modern urban environments. However, traditional methods that rely solely on visual cues face challenges, particularly in adverse conditions. The objective of this research is to enhance emergency vehicle detection by leveraging the synergies between acoustic and visual information. By incorporating advanced deep learning techniques for both acoustic and visual data, our aim is to significantly improve the accuracy and response times. To achieve this goal, we developed an attention-based temporal spectrum network (ATSN) with an attention mechanism specifically designed for ambulance siren sound detection. In parallel, we enhanced visual detection tasks by implementing a Multi-Level Spatial Fusion YOLO (MLSF-YOLO) architecture. To combine the acoustic and visual information effectively, we employed a stacking ensemble learning technique, creating a robust framework for emergency vehicle detection. This approach capitalizes on the strengths of both modalities, allowing for a comprehensive analysis that surpasses existing methods. Through our research, we achieved remarkable results, including a misdetection rate of only 3.81% and an accuracy of 96.19% when applied to visual data containing emergency vehicles. These findings represent significant progress in real-world applications, demonstrating the effectiveness of our approach in improving emergency vehicle detection systems.

Enhancing blockchain scalability and security: the early fraud detection (EFD) framework for optimistic rollups

Enhancing blockchain scalability and security: the early fraud detection (EFD) framework for optimistic rollups

ELCD-NSC2: a novel early lung cancer detection and non-small cell classification framework

ELCD-NSC2: a novel early lung cancer detection and non-small cell classification framework

Semi-supervised method for visual detection of automotive paint defects

Automotive paint defect detection plays a crucial role in the automotive production process. Current research on visual defect detection methods is mainly based on supervised learning, which requires a large number of labeled image samples for model training. The labeling work is not only time consuming but also expensive, seriously hindering the testing and application of these models in practice. To address this issue, this study proposes a new method for automotive paint defect detection based on a semi-supervised training strategy. First, a semi-supervised automotive paint defect detection framework, which can use labeled and unlabeled samples to reduce the cost of data labeling effectively, is presented. Then, a spatial pyramid pooling fast external attention module that introduces an external attention mechanism is proposed to improve the traditional YOLOv7 network structure, called YOLOv7-EA, to obtain good detection performance. This network acts as a detector to generate high-quality pseudo labels for the unlabeled samples, providing additional data to train the model; meanwhile, it performs the final detection task. Lastly, a Wise-intersection over union loss function that considers the quality of the anchor box is introduced to reduce the interference of low-quality samples and improve the convergence speed and detection accuracy of the model. Using this method, we can accomplish the task of automotive paint defect detection with a small number of labeled image samples. Experimental results on the automotive paint defect dataset show that mean average precision (mAp)@.5, mAp@.75, and mAp@.5:.95 are superior to other methods under the condition of 10% and 15% labeled data, achieving good defect detection performance.

Simultaneous detection of dental caries and fissure sealant in intraoral photos by deep learning: a pilot study

BackgroundDeep learning, as an artificial intelligence method has been proved to be powerful in analyzing images. The purpose of this study is to construct a deep learning-based model (ToothNet) for the simultaneous detection of dental caries and fissure sealants in intraoral photos.MethodsA total of 1020 intraoral photos were collected from 762 volunteers. Teeth, caries and sealants were annotated by two endodontists using the LabelMe tool. ToothNet was developed by modifying the YOLOX framework for simultaneous detection of caries and fissure sealants. The area under curve (AUC) in the receiver operating characteristic curve (ROC) and free-response ROC (FROC) curves were used to evaluate model performance in the following aspects: (i) classification accuracy of detecting dental caries and fissure sealants from a photograph (image-level); and (ii) localization accuracy of the locations of predicted dental caries and fissure sealants (tooth-level). The performance of ToothNet and dentist with 1year of experience (1-year dentist) were compared at tooth-level and image-level using Wilcoxon test and DeLong test.ResultsAt the image level, ToothNet achieved an AUC of 0.925 (95% CI, 0.880–0.958) for caries detection and 0.902 (95% CI, 0.853–0.940) for sealant detection. At the tooth level, with a confidence threshold of 0.5, the sensitivity, precision, and F1-score for caries detection were 0.807, 0.814, and 0.810, respectively. For fissure sealant detection, the values were 0.714, 0.750, and 0.731. Compared with ToothNet, the 1-year dentist had a lower F1 value (0.599, p < 0.0001) and AUC (0.749, p < 0.0001) in caries detection, and a lower F1 value (0.727, p = 0.023) and similar AUC (0.829, p = 0.154) in sealant detection.ConclusionsThe proposed deep learning model achieved multi-task simultaneous detection in intraoral photos and showed good performance in the detection of dental caries and fissure sealants. Compared with 1-year dentist, the model has advantages in caries detection and is equivalent in fissure sealants detection.

Enhancing brain tumor detection in MRI images through explainable AI using Grad-CAM with Resnet 50

This study addresses the critical challenge of detecting brain tumors using MRI images, a pivotal task in medical diagnostics that demands high accuracy and interpretability. While deep learning has shown remarkable success in medical image analysis, there remains a substantial need for models that are not only accurate but also interpretable to healthcare professionals. The existing methodologies, predominantly deep learning-based, often act as black boxes, providing little insight into their decision-making process. This research introduces an integrated approach using ResNet50, a deep learning model, combined with Gradient-weighted Class Activation Mapping (Grad-CAM) to offer a transparent and explainable framework for brain tumor detection. We employed a dataset of MRI images, enhanced through data augmentation, to train and validate our model. The results demonstrate a significant improvement in model performance, with a testing accuracy of 98.52% and precision-recall metrics exceeding 98%, showcasing the model’s effectiveness in distinguishing tumor presence. The application of Grad-CAM provides insightful visual explanations, illustrating the model’s focus areas in making predictions. This fusion of high accuracy and explainability holds profound implications for medical diagnostics, offering a pathway towards more reliable and interpretable brain tumor detection tools.

Development of a FRET aptasensor based on MoS2-doped Zn-MOF as luminophore for selective detection of cadmium in aqueous solutions.

A robust "on-off" fluorescent aptasensor was developed using nanohybrids of molybdenum sulfide (MoS2) quantum dot (QD)-doped zinc metal-organic frameworks (Zn-MOF) for selective and sensitive detection of cadmium ions (Cd2+) in water. This nanohybrid (MoS2@Zn-MOF), synthesized via "bottle around the ship" methodology, exhibited a high-intensity fluorescence emission centered at 430nm (λEm) (blue) on excitation at 320nm (λEx). Further, the conjugation of this fluorophore to phosphate-modified cadmium aptamer (Cd-2-2) was achieved through carbodiimide reaction. The hybridization of prepared sensing probe (MoS2@Zn-MOF/Cd-2-2 aptamer) was done with dabcyl-conjugated complementary DNA (cDNA), acting as energy donor-acceptor pair in the fluorescence resonance energy transfer (FRET) system. This hybridizationcauses the fluorescence quenching of the nanohybrid. In the presence of Cd2+, the aptamer from the fabricated nano-biosensing probe binds to these ions, resulting in release of dabcyl-cDNA oligomer. This release of dabcyl-cDNA oligomer from the sensing probes restores the fluorescence of the nanohybrid. Under optimized conditions (sensing probe/dabcyl-cDNA ratio 1/7, pH 7.4, and temp 28°C), the sensing probe showed a fast response time of 1min. The fluorescence intensity of the nanohybrid can be utilizedto determine the concentration of Cd2+. The proposed aptasensor achieved highly sensitive detection of Cd2+ with a limit of detection (LOD) of 0.24ppb over the range of 1 × 10-9 to 1 × 10-4M along with minimal effects of interferences (e.g., Hg2+, Pb2+, and Zn2+) and good reproducibility. The designed aptasensor based on MoS2@Zn-MOF nanofluorophore offers a highly sensitive and selective approach for rapid screening of metal ions in aqueous environments.

Cascaded framework for earthquake building damage detection combining spatial and frequency domain feature integration

Abstract. Building collapse is a major cause of casualties after an earthquake, so accurately extracting building damage information is critical for post-earthquake assessment and rescue. Currently, most deep learning methods focus on the end-to-end detection of building collapse. However, in real-world earthquake scenarios, the end-to-end computational process often lacks flexibility and struggles to meet the requirements of rapid emergency response. To address this issue, this paper proposes a cascaded framework that combines pre-earthquake building extraction and post-earthquake building damage classification. The proposed framework includes two sections: (1) Progressive building semantic segmentation model in the joint frequency domain. This model is designed to accurately extract buildings prior to an earthquake, with the goal of minimizing error propagation throughout the cascading process. The model addresses the spatial similarity of buildings under complicated backgrounds, as well as the high internal heterogeneity of buildings, by utilizing frequency domain techniques. It compensates for the shortcomings of traditional models in terms of incomplete information extraction through the effective integration of global and local information. Finally, the model employs edge priors for edge regularization. (2) Rapid building damage classification process. Based on the accurate building extraction results, a fast and efficient classification process is developed. This process uses a simple and lightweight classification network to effectively extract building damage information caused by the earthquake. The superiority of the proposed framework is validated through comparison with traditional cascading architectures and end-to-end models. The results show that the cascading framework not only provides accurate pre-earthquake building extraction, but also enables efficient and accurate post-earthquake damage classification, which meets the requirements of rapid post-earthquake emergency response. This balance of accuracy and speed is essential for effective disaster management and recovery.

A novel framework of DDoS attack detection in network using hybrid heuristic deep learning approaches with attention mechanism

The “Distributed Denial of Service (DDoS)” threats have become a tool for the hackers, cyber swindlers, and cyber terrorists. Despite the high amount of conventional mitigation mechanisms that are present nowadays, the DDoS threats continue to enhance in severity, volume, and frequency. The DDoS attack has highly affected the availability of the networks for the previous years and still, there is no efficient defense technique against it. Moreover, the new and complex DDoS attacks are increasing on a daily basis but the traditional DDoS attack detection techniques cannot react to these threats. On the other hand, the hackers are employing very innovative strategies to initiate the threats. But, the traditional methods can become effective and reliable when combined with the deep learning-aided approaches. To solve these certain issues, a framework detection mechanism for DDoS attacks utilizes an attention-aided deep learning methodology. The primary thing is the acquisition of data from standard data online sources. Further, from the garnered data, the significant features are drawn out from the “Deep Weighted Restricted Boltzmann Machine (RBM)” using a “Deep Belief Network (DBN)”, in which the parameters are tuned by employing the recommended Enhanced Gannet Optimization Algorithm (EGOA). This feature extraction operation increases the network performance rate and also diminishes the dimensionality issues. Lastly, the acquired features are transferred to the model of “Attention and Cascaded Recurrent Neural Network (RNN) with Residual Long Short Term Memory (LSTM) (ACRNN-RLSTM)” blocks for the DDoS threat detection purpose. This designed network precisely identifies the complex and new attacks, thus it increases the trustworthiness of the network. In the end, the performance of the approach is contrasted with other traditional algorithms. Hence, the simulation outcomes are obtained that prove the system’s efficiency. Also, the outcomes displayed that the designed system overcame the conventional threat detection techniques.

Real-time detection of surface cracking defects for large-sized stamped parts

This study presents a framework for the real-time detection of surface cracking in large-sized stamped metal parts. The framework aims to address the challenges of low detection efficiency and high error rates associated with manual cracking detection. Within this framework, a novel network, SNF-YOLOv8, is proposed to efficiently detect cracking while ensuring that the detection speed matches the production speed. The network incorporates a convolutional spatial-to-depth module to enhance the detection of small-sized cracking and mitigate surface interference during inspections. Furthermore, a visual self-attention mechanism is introduced to improve feature extraction. A combination of standard convolutional and depth-wise separable convolutional layers in the neck network enhances speed without compromising accuracy. Experimental validation conducted using a dataset from actual production lines, in collaboration with a multi-national corporation, demonstrates that SNF-YOLOv8 achieves an average precision of 85.2% at a detection speed of 164 frames per second. The framework achieves an accuracy rate of 98.8% in detecting large-sized cracking and 96.4% in detecting small-sized cracking, meeting the requirements for high-precision and real-time detection applications.

PermDroid a framework developed using proposed feature selection approach and machine learning techniques for Android malware detection

The challenge of developing an Android malware detection framework that can identify malware in real-world apps is difficult for academicians and researchers. The vulnerability lies in the permission model of Android. Therefore, it has attracted the attention of various researchers to develop an Android malware detection model using permission or a set of permissions. Academicians and researchers have used all extracted features in previous studies, resulting in overburdening while creating malware detection models. But, the effectiveness of the machine learning model depends on the relevant features, which help in reducing the value of misclassification errors and have excellent discriminative power. A feature selection framework is proposed in this research paper that helps in selecting the relevant features. In the first stage of the proposed framework, t-test, and univariate logistic regression are implemented on our collected feature data set to classify their capacity for detecting malware. Multivariate linear regression stepwise forward selection and correlation analysis are implemented in the second stage to evaluate the correctness of the features selected in the first stage. Furthermore, the resulting features are used as input in the development of malware detection models using three ensemble methods and a neural network with six different machine-learning algorithms. The developed models’ performance is compared using two performance parameters: F-measure and Accuracy. The experiment is performed by using half a million different Android apps. The empirical findings reveal that malware detection model developed using features selected by implementing proposed feature selection framework achieved higher detection rate as compared to the model developed using all extracted features data set. Further, when compared to previously developed frameworks or methodologies, the experimental results indicates that model developed in this study achieved an accuracy of 98.8%.

Cross-modal change detection flood extraction based on self-supervised contrastive pre-training

Abstract. Flood extraction is a critical issue in remote sensing analysis. Accurate flood extraction faces challenges such as complex scenes, image differences across modalities, and a shortage of labeled samples. Traditional supervised deep learning algorithms demonstrate promising prospects in flood extraction. They mostly rely on abundant labeled data. However, in practical applications, there is a scarcity of available labeled samples for flood change regions, leading to an expensive acquisition of such data for flood extraction. In contrast, there is a wealth of unlabeled data in remote sensing images. Self-supervised contrastive learning (SSCL) provides a solution, allowing learning from unlabeled data without explicit labels. Inspired by SSCL, we utilized the open-source CAU-Flood dataset and developed a framework for cross-modal change detection in flood extraction (CMCDFE). We employed the Barlow Twin (BT) SSCL algorithm to learn effective visual feature representations of flood change regions from unlabeled cross-modal bi-temporal remote sensing data. Subsequently, these well-initialized weight parameters were transferred to the task of flood extraction, achieving optimal accuracy. We introduced the improved CS-DeepLabV3+ network for extracting flood change regions from cross-modal bi-temporal remote sensing data, incorporating the CBAM dual attention mechanism. By demonstrating on the CAU-Flood dataset, we proved that fine-tuning with only a pre-trained encoder can surpass widely used ImageNet pre-training methods without additional data. This approach effectively addresses downstream cross-modal change detection flood extraction tasks.

Fake User Detection Based on Multi-Model Joint Representation

The existing deep learning-based detection of fake information focuses on the transient detection of news itself. Compared to user category profile mining and detection, transient detection is prone to higher misjudgment rates due to the limitations of insufficient temporal information, posing new challenges to social public opinion monitoring tasks such as fake user detection. This paper proposes a multimodal aggregation portrait model (MAPM) based on multi-model joint representation for social media platforms. It constructs a deep learning-based multimodal fake user detection framework by analyzing user behavior datasets within a time retrospective window. It integrates a pre-trained Domain Large Model to represent user behavior data across multiple modalities, thereby constructing a high-generalization implicit behavior feature spectrum for users. In response to the tendency of existing fake user behavior mining to neglect time-series features, this study introduces an improved network called Sequence Interval Detection Net (SIDN) based on Sequence to Sequence (seq2seq) to characterize time interval sequence behaviors, achieving strong expressive capabilities for detecting fake behaviors within the time window. Ultimately, the amalgamation of latent behavioral features and explicit characteristics serves as the input for spectral clustering in detecting fraudulent users. The experimental results on Weibo real dataset demonstrate that the proposed model outperforms the detection utilizing explicit user features, with an improvement of 27.0% in detection accuracy.

A 2D Acridine‐Based Covalent Organic Framework for Selective Detection and Efficient Extraction of Gold from Complex Aqueous‐Based Matrices

AbstractDeveloping candidate materials which possess the ability of both selective detection and efficient capture of precious metal gold is highly desirable for environment and economy. However, most of reported materials only focus on single function, which seriously restricts their practical application as probes or adsorbents. Herein, a two dimensional (2D) acridine‐based covalent organic framework (TpDa‐COF) is prepared via the linkage of imine bonds for gold detection and adsorption. The synthesized COF can achieve both fluorescence and colorimetric dual sensing for Au3+ in a low concentration range (0.1–1.5 ppm) with the limit of detection (LOD) of 0.036 ppm. Impressively, the selectivity of TpDa‐COF for the detection of Au3+ is admirable (Fe3+, Fe2+ and Cu2+ for negligible influence on its fluorescence). In addition, TpDa‐COF exhibits ultrahigh adsorption capacity of 982.5 mg ⋅ g−1 for gold at pH=4, which is attributed the synergistic effect of both selective coordination and reductive process of Au(III) to Au(0). Meanwhile, both positive entropy change (ΔS=76.07 J ⋅ mol−1 ⋅ K−1) and high distribution coefficient (Kd=12484.8 mL ⋅ g−1) confirm the good affinity between TpDa‐COF framework and gold. This work gives us a new insight to prepare COF with pyridine nitrogen sites for gold detection and adsorption.

A 2D Acridine-Based Covalent Organic Framework for Selective Detection and Efficient Extraction of Gold from Complex Aqueous-Based Matrices.

Developing candidate materials which possess the ability of both selective detection and efficient capture of precious metal gold is highly desirable for environment and economy. However, most of reported materials only focus on single function, which seriously restricts their practical application as probes or adsorbents. Herein, a two dimensional (2D) acridine-based covalent organic framework (TpDa-COF) is prepared via the linkage of imine bonds for gold detection and adsorption. The synthesized COF can achieve both fluorescence and colorimetric dual sensing for Au3+ in a low concentration range (0.1-1.5 ppm) with the limit of detection (LOD) of 0.036 ppm. Impressively, the selectivity of TpDa-COF for the detection of Au3+ is admirable (Fe3+, Fe2+ and Cu2+ for negligible influence on its fluorescence). In addition, TpDa-COF exhibits ultrahigh adsorption capacity of 982.5 mg ⋅ g-1 for gold at pH=4, which is attributed the synergistic effect of both selective coordination and reductive process of Au(III) to Au(0). Meanwhile, both positive entropy change (ΔS=76.07 J ⋅ mol-1 ⋅ K-1) and high distribution coefficient (Kd=12484.8 mL ⋅ g-1) confirm the good affinity between TpDa-COF framework and gold. This work gives us a new insight to prepare COF with pyridine nitrogen sites for gold detection and adsorption.

Flower-like 3D SnS decorated on nickel metal-organic framework for electrochemical detection of dimetridazole in food samples

Dimetridazole (DMZ) is a broad-spectrum antibiotic effective against bacterial and protozoan infections in humans and poultry farms. However, excessive DMZ intake leads to harmful effects. Thus, minimizing its environmental presence is crucial for sustaining daily life. This study presents an innovative approach to construct flower-like SnS particle decorations on a nickel metal-organic framework (Ni-MOF@SnS) as an electrocatalyst for DMZ detection. The Ni-MOF@SnS/GCE sensor exhibits exceptional electrocatalytic behavior, including a significantly reduced detection limit of 1.6 nM, extensive linear ranges from 0.01 μM to 60 μM and from 60 μM to 231 μM at lower and higher DMZ concentrations, respectively. It also shows enhanced sensitivity (0.139 μA μM−1 cm−2) and remarkable selectivity for DMZ detection using differential-pulse voltammetry (DPV). Furthermore, the proposed sensor demonstrates good recovery results with actual food samples.

Carbon dots-functionalized metal−organic framework for the dual detection of tetracycline and norfloxacin

Tetracycline and norfloxacin are two important antibiotics known for their effective treatment of inflammation. However, their excessive use inevitably leads to serious environmental hazards. Rapid and highly sensitive detection of their trace amounts is crucial for early prevention of related diseases. While there are numerous reported fluorescent probes capable of highly sensitive detection of tetracycline and norfloxacin individually, very few can simultaneously detect both. Developing a probe capable of detecting both antibiotics simultaneously is particularly necessary to enhance detection efficiency. In this study, carbon quantum dots derived from fragrans were functionalized with Metal-Organic Framework (MOF) materials. This successful approach enabled simultaneous detection of both tetracycline and norfloxacin, with detection limits of 0.15 μM and 0.87 μM, respectively. Furthermore, successful detections were achieved in water, milk and soil samples, yielding excellent results.