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SECURE MEDICAL image RETRIEVAL USING FAST image processing ALGORITHMS

SCALABLE COMPUTING-PRACTICE AND EXPERIENCE 2024年第5期25卷 4323-4334页

作者： Lafta, Sameer Abdulsttar Rafash, Amaal Ghazi Hamad Al-falahi, Noaman Ahmed Yaseen Hussein, Hussein Abdulqader Abdulkareem, Mohanad Mahdi Middle Tech Univ Tech Instructors Training Inst Baghdad Iraq Iraqi Minist Commun Digital Transformat Dept Baghdad Iraq Iraqi Minist Educ Data Ctr Management Dept Baghdad Iraq

Content Based image Retrieval (CBIR) is a relatively new idea in the field of real-time image retrieval applications;it is a framework for retrieving pictures from diverse medical imaging sources using a variety of image-related attributes, such as color, texture, and form. Using both single and multiple input queries, CBIR processes semantic data or the same object for various class labels in the context of medical image retrieval. Due to the ambiguity of image search, optimizing the retrieval of a query picture by comparing it across numerous image sources may be problematic. The goal is to find a way to optimize the process by which requested images are retrieved from various storage locations. To effectively extract medical images, we propose a hybrid framework (consisting of deep convolution neural networks (DCNN) and the Pareto Optimization technique). In order to obtain medical pictures, a DCNN is trained on them, and then its properties and classification results are employed. Explore enhanced effective medical picture retrieval by using a Pareto optimization strategy to eliminate superfluous and dominant characteristics. When it comes to retrieving images by query from various picture archives, our method outperforms more conventional methods. Use the jargon of machine learning to propose a Novel Unsupervised Label Indexing (NULI) strategy for retrieving picture labels. To enhance the effectiveness of picture retrieval, we characterize machine learning as a matrix convex optimization using a cluster rebased matrix representation. We describe an empirical investigation on many medical picture datasets, finding that the searchbased image annotation (SBIA) schema benefits from our suggested method. As a result, CT images of the lung region are explored in this study by constructing a content-based image retrieval system using various machine learning and Artificial Intelligence techniques. real-world applications of medical imaging are becoming more significan

关键词： Medical image image retrieval image processing

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Recent Advances in Applying Machine learning and deep learning to Detect Upper Gastrointestinal Tract Lesions

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IEEE ACCESS 2023年 11卷 66544-66567页

作者： Vania, Malinda Tama, Bayu Adhi Maulahela, Hasan Lim, Sunghoon Ulsan Natl Inst Sci & Technol Dept Ind Engn Ulsan 44919 South Korea Ulsan Natl Inst Sci & Technol Ind Intelligentizat Inst Ulsan 44919 South Korea Univ Maryland NSF HDR Inst Harnessing Data & Model Revolut Pola Baltimore MD 21250 USA Univ Indonesia Cipto Mangunkusumo Natl Gen Hosp Div Gastroenterol Dept Internal MedFac Med Jakarta 10430 Indonesia Ulsan Natl Inst Sci & Technol Grad Sch Artificial Intelligence Ulsan 44919 South Korea

The clinical application of a real-time artificial intelligence (AI) image processing system to diagnose upper gastrointestinal (GI) malignancies remains an experimental research and engineering problem. Understanding these commonly used technical techniques is required to appreciate the scientific quality and novelty of AI studies. Clinicians frequently lack this technical background, and AI experts may be unaware of such clinical relevance and implications in daily practice. As a result, there is a growing need for a multidisciplinary, international assessment of how to conduct high-quality AI research in upper GI malignancy detection. This research will help endoscopists build approaches or models to increase diagnosis accuracy for upper GI malignancies despite variances in experience, education, personnel, and resources, as it offers real-time and retrospective chances to improve upper GI malignancy diagnosis and screening. This comprehensive review sheds light on potential enhancements to computer-aided diagnostic (CAD) systems for GI endoscopy. The survey includes 65 studies on automatic upper GI malignancy diagnosis and evaluation, which are compared by endoscopic modalities, image counts, models, validation methods, and results. The main goal of this research is to assess and compare each AI method's current stage and potential improvement to boost performance, maturity, and the possibility to open new research areas for the application of a real-time AI image recognition system that diagnoses upper GI malignancies. The findings of this study suggest that Support Vector Machines (SVM) are frequently utilized in gastrointestinal (GI) image processing within the context of machine learning (ML). Moreover, the analysis reveals that CNN-based supervised learning object detection models are widely employed in GI image analysis within the deep learning (DL) context. The results of this study also suggest that RGB is the most commonly used image modality for GI ana

关键词： Endoscopy upper gastrointestinal tract artificial intelligence machine learning deep learning

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Automatic Lung Segmentation in Chest X-Ray images Using SAM With Prompts From YOLO

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IEEE ACCESS 2024年 12卷 122805-122819页

作者： Khalili, Ebrahim Priego-Torres, Blanca Leon-Jimenez, Antonio Sanchez-Morillo, Daniel Univ Cadiz Dept Engn Automat Elect & Comp Architecture & Netw Puerto Real 11519 Spain Biomed Res & Innovat Inst Cadiz INiBICA Cadiz 11009 Spain Puerta del Mar Univ Hosp Pulmonol Dept Cadiz 11009 Spain

Despite the impressive performance of current deep learning models in the field of medical imaging, transferring the lung segmentation task in X-ray images to clinical practice is still a pending task. In this study, the performance of a fully automatic framework for lung field segmentation in chest X-ray images was evaluated. The framework is rooted in the combination of the Segment Anything Model (SAM) with prompt capabilities, and the You Only Look Once (YOLO) model to provide effective prompts. Transfer learning, loss functions, and several validation strategies were thoroughly assessed. This provided a complete benchmark that enabled future research studies to fairly compare new segmentation strategies. The results achieved demonstrated significant robustness and generalization capability against the variability in sensors, populations, disease manifestations, device processing, and imaging conditions. The proposed framework was computationally efficient, could address bias in training over multiple datasets, and had the potential to be applied across other domains and modalities.

关键词： Lung image segmentation Solid modeling Data models X-ray imaging YOLO Biomedical imaging deep learning Biomedical X-ray imaging image segmentation lung deep learning

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Sustainable Fog-Assisted Intelligent Monitoring Framework for Consumer Electronics in Industry 5.0 Applications

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IEEE TRANSACTIONS ON CONSUMER ELECTRONICS 2024年第1期70卷 1501-1510页

作者： Tripathy, Subhranshu Sekhar Bebortta, Sujit Gadekallu, Thippa Reddy KIIT Deemed Univ Sch Comp Engn Bhubaneswar 751024 India Ravenshaw Univ Dept Comp Sci Cuttack 753003 India Univ Relig & Denominat Dept Interdisciplinary Studies Qom *** Iran Lebanese Amer Univ Dept Elect & Comp Engn Byblos 11022801 Lebanon Vellore Inst Technol Sch Informat Technol & Engn Vellore 632014 India Jiaxing Univ Coll Informat Sci & Engn Jiaxing 314001 Peoples R China Lovely Profess Univ Div Res & Dev Phagwara 144001 India

The fifth era of the industry (Industry 5.0) has been marked by the reformation witnessed in consumer electronics sector by bringing forth technology that could enhance efficiency, connectivity, and user experience. Industry 5.0 makes it possible to create intelligent consumer electronics products that can interact, analyse data, and instantly adjust to user preferences. Fog processing further enhances Industry 5.0 by bringing processing power closer to end-user devices at the network's edge. Traditional machine learning techniques are unsuitable for manufacturing use cases which demand high degree of interoperability and heterogeneity due to the unavailability of private data, which requires decentralized learning solutions. To address this, we designed a monitoring framework that uses deep reinforcement learning to predict the effect of mobile computing resources in manufacturing systems and detect disruptions in real time. Our framework is deployed at the Fog computing level and includes a dynamic rescheduling module that sustainably optimizes task assignment, improves execution accuracy, reduces delay, and maximizes the resource utilization. Numerical results demonstrate the efficiency of our scheme in managing task rescheduling and real-time disruption detection, depicting the sustainable utilization of available resources over the considered benchmark algorithms.

关键词： Edge computing Industries Monitoring Cloud computing real-time systems Task analysis Data analysis Industry 5.0 consumer electronics deep reinforcement learning fog computing task scheduling sustainability resource utilization

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A Novel deep learning-Based Approach for Defect Detection of Synthetic Leather Using Gaussian Filtering

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IEEE ACCESS 2024年 12卷 196702-196714页

作者： Mai, Christopher Penava, Pascal Buettner, Ricardo Univ Fed Armed Forces Hamburg Helmut Schmidt Univ Chair Hybrid Intelligence D-22043 Hamburg Germany

Synthetic leather is a commonly used material, especially in the clothing industry. It is employed in the production of footwear, such as shoes and boots, as well as handbags and other accessories. Prior to exportation, the leather is subjected to a series of processing stages, which may result in the formation of surface defects. Quality assurance, which serves to identify defective leather in a timely manner, is still partially conducted by humans. This step is time-consuming and is associated with a higher error rate. To address these challenges, the deployment of image processing systems for the automated inspection of leather defects is becoming increasingly crucial. The objective of this study is to develop an effective deep learning model for the classification of defective synthetic leather, with the aim of implementing it in the quality control of synthetic leather production. To this end, a variety of architectural approaches are employed, including Xception, InceptionV3, ResNet50V2, VGG19, and VGG16. In addition to hyperparameter tuning, all architectures utilize transfer learning. Moreover, the impact of the Gaussian filter as a pre-processing step for images is examined. The results show that by using an innovative Gaussian filtering approach, an accuracy of 100% was achieved with the VGG16 model. Furthermore, the results show that the filtering approach has a positive influence on the accuracy of all other models. The high accuracies of the deep learning models show that the use of machine vision systems for the automatic inspection of defects in the synthetic leather industry is an effective economic step for companies, as it leads to cost and time savings in production due to the high classification performance. This study provides an overview of the results achieved and other key performance indicators for all the models used.

关键词： Production Transfer learning deep learning Defect detection Accuracy Inspection Information filters Supervised learning Gaussian filter synthetic leather defect detection transfer learning pre-trained architectures data augmentation hyperparameter tuning

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Research on weld seam feature extraction and defect identification technology

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NONDESTRUCTIVE TESTING AND EVALUATION 2024年

作者： Shang, Fei Cai, Dandan Sun, Bo Han, Yongquan Yang, Mingyuan Zhan, Chunying Inner Mongolia Univ Sci & Technol Sch Mech Engn Baotou Peoples R China Inner Mongolia Univ Sci & Technol Inner Mongolia Key Lab Intelligent Diag & Control Baotou Peoples R China Inner Mongolia Univ Sci & Technol Sch Mat & Met Baotou Peoples R China Inner Mongolia Inst Special Equipment Inspect & Re Baotou Branch Baotou Peoples R China Baotou North Sait Testing Technol Co Special Equipment Inspect & Testing Ctr Baotou Peoples R China

The quality of weld seams is a core factor affecting the performance of welded components, significantly impacting the safety and reliability of weldment structures. It holds considerable importance in quality control and cost management. Therefore, weld seam inspection is an indispensable part of industrial manufacturing and a key topic in non-destructive testing. This paper comprehensively summarises the main research results in weld seam feature extraction and defect identification based on systematic and detailed literature research. Firstly, the paper compares current mainstream non-destructive testing technologies, such as ultrasonic testing, radiographic testing, and infrared thermography, regarding detection principles, performance accuracy, and efficiency. It summarises their applicable scopes, as well as their advantages and disadvantages, and briefly introduces image processing-based methods for weld seam feature extraction. Secondly, a discussion and analysis of weld defect recognition are carried out, focusing on intelligent recognition methods based on deep learning, such as convolutional neural networks, generative adversarial networks, YOLO, etc. Finally, the outlook and future research trends are presented from various perspectives, such as real-time, adaptability, intelligence, etc. to provide a theoretical basis and technical support for welding quality control and structural safety assessment.

关键词： Weld seam inspection defect recognition non-destructive testing image processing feature extraction deep learning

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An improved deep transfer learning approach to identify the human face mask in real-time considering the COVID-19 pandemic

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MULtimeDIA TOOLS AND APPLICATIONS 2024年第7期83卷 21695-21743页

作者： Rusia, Mayank Kumar Singh, Dushyant Kumar Motilal Nehru Natl Inst Technol Allahabad Dept Comp Sci & Engn Prayagraj Uttar Pradesh India

Face recognition systems have always been in great demand for various security-based applications, authentication being one of them. Due to the continuous upsurge of the COVID-19 pandemic and emerging variants of coronaviruses, wearing a face mask has been made mandatory in many countries, especially in crowded places. This situation poses significant challenges to the face recognition systems in recognizing the person's identity with face mask-based partial occlusion. Therefore, an update is needed in the traditional face recognition systems to ascertain whether the person is wearing a mask. This manuscript offers a novel Fiducial Point-based Non-local Means De-Noising (FP-NMDN) method for data pre-processing. This manuscript also proposed two comprehensive feature extraction mechanisms, i.e., transfer learning-based models and a customized Convolutional Neural Network (CNN) model. The experiment is conducted for five popular baseline architectures viz. Visual Geometry Group (VGG16), Residual Network (ResNet50), MobileNetV2, InceptionV3, and EfficientNetB0 with fine-tuning of hyperparameters and a customized CNN architecture. A modified dense network with a new classification layer has been introduced to obtain high classification results in less inference time. The datasets are collected from four valid sources;Kaggle Medical Masked Face, real-world Masked Face, Face Mask, and open-source datasets that have been resynthesized based on predefined experimental criteria named Dataset-I and other existing datasets as Dataset-II. The experimental results reveal that our optimized transfer learning-based ResNet50 model achieves the best accuracy of 99.68% and 99.67% for Dataset-I and Dataset-II, respectively. Besides, our customized CNN model outperforms other recent methods regarding overhead and inference time.

关键词： COVID-19 Partial occlusion Biometrics Computer vision Transfer learning image processing

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Object classification and visualization with edge artificial intelligence for a customized camera trap platform

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ECOLOGICAL INFORMATICS 2024年 79卷

作者： Nazir, Sajid Kaleem, Mohammad Glasgow Caledonian Univ Sch Comp Engn & Built Environm Glasgow City Scotland COMSATS Univ Dept Elect & Comp Engn Islamabad Pakistan Glasgow Caledonian Univ Cowcaddens Rd Glasgow G4 0BA Scotland

The camera traps have revolutionized the image and video capture in ecology and are often used to monitor and record animal presence. With miniaturization of low power electronic devices, better battery technologies, and software advancements, it has become possible to use the edge devices, such as Raspberry Pi as camera traps that can not only capture images and videos, but can also enable sophisticated image processing, and off-site communications. These developments can help to provide near real-time insights and reduce the manual processing of images. The on-board image classification and visualization is facilitated by the advancements in the deep Neural Networks (DNN), transfer learning approaches, and software libraries. This paper provides an investigation of image classification with transfer learning approaches using pretrained DNN models, and visualizations with Explainable Artificial Intelligence (XAI) techniques on Raspberry Pi Zero (RPi-Z) edge device. The MobileNetV2 model was used for image classification on the Florida-Part1 dataset obtaining the results for precision, recall, and F1-score as 0.95, 0.96, and 0.95 respectively. We also compared the model performance of MobileNetV2, EfficientNetV2B0, and MobileViT models for classification on the Extinction dataset with the best results for precision, recall, and F1-score as 0.97, 0.96, and 0.96 respectively, obtained with the EfficientNetV2B0 model. Two XAI techniques, Gradient-weighted-Class Activation Mapping (Grad-CAM) and Occlusion Sensitivity were used for visualization through heatmaps, to highlight the relative importance of the image areas contributing to the DNN model's prediction, that can also help to understand the model's performance and bias. The results provide practical use case scenarios for utilizing the transfer learning approaches, model optimization and deployment to edge devices, and model visualizations in ecological research.

关键词： Data science Computer vision deep learning Model generalization Fine tuning Explainable AI Hyperparameter tuning Vision transformers

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MASK-BASED INVISIBLE BACKDOOR ATTACKS ON OBJECT DETECTION 31

MASK-BASED INVISIBLE BACKDOOR ATTACKS ON OBJECT DETECTION

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2024 International Conference on image processing

作者： Shin, Jeongjin

ISBN: (纸本)9798350349405;9798350349399

deep learning models have achieved unprecedented performance in the domain of object detection, resulting in breakthroughs in areas such as autonomous driving and security. However, deep learning models are vulnerable to backdoor attacks. These attacks prompt models to behave similarly to standard models without a trigger;however, they act maliciously upon detecting a predefined trigger. Despite extensive research on backdoor attacks in image classification, their application to object detection remains relatively underexplored. Given the widespread application of object detection in critical real-world scenarios, the sensitivity and potential impact of these vulnerabilities cannot be overstated. In this study, we propose an effective invisible backdoor attack on object detection utilizing a mask-based approach. Three distinct attack scenarios were explored for object detection: object disappearance, object misclassification, and object generation attack. Through extensive experiments, we comprehensively examined the effectiveness of these attacks and tested certain defense methods to determine effective countermeasures. Code is available at https://***/jeongjin0/invisible- backdoor-object-detection

关键词： Backdoor attack invisible attack object detection security in deep learning

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DRL-Based Joint Task Scheduling and Trajectory Planning Method for UAV-Assisted MEC Scenarios

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IEEE ACCESS 2024年 12卷 156224-156234页

作者： Gu, Cheng Li, Fan Liu, Dong-Sheng Wu, Yi-Xuan Wang, He-Xing Wuhan Univ Sch Informat Management Wuhan 430072 Peoples R China Inner Mongolia Univ Sch Comp Sci Hohhot 010000 Peoples R China Inner Mongolia Univ Coll Software Hohhot 010000 Peoples R China Northeastern Univ Qinhuangdao Sch Comp & Commun Engn Qinhuangdao 066004 Peoples R China

The proliferation of Internet of Things (IoT) devices has resulted in a massive increase in data generation, necessitating robust solutions for real-time data processing and analysis. The integration of Unmanned Aerial Vehicles (UAVs) with MEC systems presents a promising enhancement, providing dynamic, mobile edge computing capabilities that can adapt to changing conditions and demands. However, efficiently managing the task offloading and trajectory planning of UAVs in such scenarios poses significant challenges, particularly in maximizing coverage while minimizing time and energy consumption. In this context, this paper proposes a novel UAV-based MEC model utilizing the Multi-Agent deep Deterministic Policy Gradient (MADDPG) reinforcement learning algorithm to optimize task scheduling and UAV trajectory in real-time. Our model enables UAVs to function as mobile edge servers, dynamically processing and routing tasks between terminal devices and edge servers based on real-time device demands. By incorporating a sophisticated reward function that prioritizes the minimization of system costs-including energy, time, and throughput maximization-our approach not only enhances the operational efficiency of UAV-assisted MEC systems but also improves the quality and responsiveness of edge computing services. After training the MADDPG model in the simulated environment, the experiments show that our approach significantly improved the performance of UAV-assisted MEC systems in real-time scenarios, converging after 50 learning episodes and achieving 90% task completion rate.

关键词： Autonomous aerial vehicles Servers Signal to noise ratio Computational modeling real-time systems Internet of Things Resource management Processor scheduling Data models Trajectory planning Mobile edge computing UAV deep deterministic policy gradient task offloading trajectory planning

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