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MFENet: Multi-scale and Local Frequency Enhancement Network for Skin Lesion Classification 1

41st computer Graphics International Conference, CGI 2024

作者： Jin, Yuyu Xiao, Zhiyong Yuan, Jiaqi He, Mingwei Liu, Li Yu, Feng Jiang, Minghua School of Computer Science and Artificial Intelligence Wuhan Textile University Wuhan China School of Electrical and Electronic Engineering Nanyang Technological University Singapore Singapore Engineering Research Center of Hubei Province for Clothing Information Wuhan China

ISBN: (数字)9783031820243

ISBN: (纸本)9783031820236

Dermatoscopy is a common diagnostic tool used by dermatologists to examine skin lesions. However, existing classification methods often rely on spatial domain feature extraction. Dermatoscopic images are frequently affected by various sources of noise, such as hair and pigmented areas, resulting in non-robust feature extraction. Therefore, relying solely on spatial domain feature extraction is limited and cannot fully differentiate the subtle features of skin lesions. This paper proposes a multi-scale and local frequency enhancement network (MFENet) for skin lesion, which significantly improves the diagnostic accuracy and interpretability of dermatoscopic images. Specifically, we introduce the multi-scale and local of frequency (MLF) module to reduce noise interference and enhance edge, shape, and texture features. The module selects low-frequency information and suppresses high-frequency noise at different scales. It further enhances details and texture information in the image by processing local frequency blocks. This makes important morphological features, such as the shape and edge, more prominent in diagnosis. Additionally, a lesion channel attention (LCA) module is proposed to further optimize the extraction and amplification of key features by learning dependencies between channels. The results show that the algorithm achieves optimal performance on the ISIC 2017 and HAM10000 datasets. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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An Integrated Analysis of Yield Prediction Models:A Comprehensive Review of Advancements and Challenges

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computers, Materials & Continua 2024年第7期80卷 389-425页

作者： Nidhi Parashar Prashant Johri Arfat Ahmad Khan Nitin Gaur Seifedine Kadry School of Computer Science and Engineering Galgotias UniversityGreater Noida203201India Department of Applied Data Science Noroff University CollegeKristiansand4612Norway Artificial Intelligence Research Center(AIRC) Ajman UniversityAjman346United Arab Emirates MEU Research Unit Middle East UniversityAmman11831Jordan Department of Computer Science College of ComputingKhon Kaen UniversityKhon Kaen40002Thailand

The growing global requirement for food and the need for sustainable farming in an era of a changing climate and scarce resources have inspired substantial crop yield prediction *** learning(DL)and machine learning(ML)models effectively deal with such *** research paper comprehensively analyses recent advancements in crop yield prediction from January 2016 to March *** addition,it analyses the effectiveness of various input parameters considered in crop yield prediction *** conducted an in-depth search and gathered studies that employed crop modeling and AI-based methods to predict crop *** total number of articles reviewed for crop yield prediction using ML,meta-modeling(Crop models coupled with ML/DL),and DL-based prediction models and input parameter selection is *** conduct the research by setting up five objectives for this research and discussing them after analyzing the selected research *** study is assessed based on the crop type,input parameters employed for prediction,the modeling techniques adopted,and the evaluation metrics used for estimatingmodel *** also discuss the ethical and social impacts of AI on ***,various approaches presented in the scientific literature have delivered impressive predictions,they are complicateddue to intricate,multifactorial influences oncropgrowthand theneed for accuratedata-driven ***,thorough research is required to deal with challenges in predicting agricultural output.

关键词： Machine learning crop yield prediction deep learning remote sensing long short-term memory time series prediction systematic literature review

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Urban green space extraction from BJ-2 remote sensing image based SegFormer semantic segmentation model (vol 51,2439835 ,2025)

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CANADIAN JOURNAL OF REMOTE SENSING 2025年第1期51卷

作者： Guo, Yan Shao, QuanSen Liu, FuJiang Lin, Weihua Wang, Mianzhi Li, Zhuowu Chen, Shuo Liu, Hongchen Su, Junshen Wang, Xianbin School of Computer Science China University of Geosciences (Wuhan) Wuhan China School of Geography and Information Engineering China University of Geosciences (Wuhan) Wuhan China China Geological Survey Xining Comprehensive Survey Center of Natural Resources Xining China Piesat Information Technology Co. Ltd Beijing China

Accurate extraction of urban green spaces is of great significance for promoting urban planning and management, and promoting sustainable urban development. Given the characteristic spatial distribution of urban green spaces in high-resolution remote sensing imagery, a framework for automated extraction of urban green spaces using the SegFormer semantic segmentation network is proposed. The framework leverages self-attention mechanisms to capture global information and enhance the extraction of green spaces. We have constructed an urban green space dataset that includes four study areas: Ruyuan, Tongcheng, Jianli, and Hanchuan. Experimental results demonstrate that the proposed architecture achieves superior performance compared with other models, with a mean intersection over union ratio of 89.26% and an accuracy rate of 95.34% (pixel accuracy). The visualization results exhibit exceptional performance in extracting green spaces with clear spatial relationships, such as roadside and community greening while effectively distinguishing between urban green spaces and farmland. Furthermore, we conduct experiments on generalization ability on four different areas. The results show that the generalization ability of SegFormer outperforms other models. Lastly, the experiments verify the impact of different data augmentation multiples on model accuracy, providing valuable insights to guide data augmentation strategies in practical engineering. © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： Semantic Segmentation

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Feature Enhancer Segmentation Network (FES-Net)for Vessel Segmentation

Feature Enhancer Segmentation Network (FES-Net)for Vessel Se...

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2023 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2023

作者： Khan, Tariq M. Arsalan, Muhammad Iqbal, Shahzaib Razzak, Imran Meijering, Erik School of Computer Science & Engineering Unsw Sydney Australia Qatar University Kindi Center for Computing Research Doha2713 Qatar Department of Electrical and Computer Engineering Islamabad Pakistan

ISBN: (纸本)9798350382204

Diseases such as diabetic retinopathy and age-related macular degeneration pose a significant risk to vision, highlighting the importance of precise segmentation of retinal vessels for tracking and diagnosis of progression. However, existing vessel segmentation methods that heavily rely on encoder-decoder structures struggle to capture contextual information about retinal vessel configurations, leading to challenges in reconciling semantic disparities between encoder and decoder features. To address this, we propose a novel feature enhancement segmentation network (FES-Net) that achieves accurate pixel-wise segmentation without requiring additional image enhancement steps. FES-Net directly processes the input image and utilizes four prompt convolutional blocks (PCBs) during downsampling, complemented by a shallow upsampling approach to generate a binary mask for each class. We evaluate the performance of FES-Net on four publicly available state-of-the-art datasets: DRIVE, STARE, CHASE, and HRF. The evaluation results clearly demonstrate the superior performance of FES-Net compared to other competitive approaches documented in the existing literature. © 2023 IEEE.

关键词： Image enhancement

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Decision Support System for Predicting Ventricular Arrhythmias Using Non-linear Features of ECG Signals

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SN computer science 2024年第4期5卷 357页

作者： Mohanty, Monalisa Dash, Pratyusa Sabut, Sukant Department of Center of Internet of Things ITER SOA University Bhubaneswar India Department of Computer Science and Engineering Heritage Institute of Technology Kolkata India School of Electronics Engineering KIIT Deemed to be University Bhubaneswar India

Automated methods using computer-aided decision-making process are effectively used for timely detection of VAs which are the most life-threatening conditions. In this work, we have proposed a decision support system for detection of ventricular arrhythmias (VAs) with a low computational complexity using hybrid features derived from three different transform techniques i.e. DWT, EEMD, and VMD. The methods mainly consist of a windowing technique, signal decomposition, feature extraction, and classification. About 24 time–frequency based features were extracted with 22,721 × 24 and ranked for selection of higher ranked features having maximum information of disease. The reduced feature set consists of only 6 number of highly ranked features which are then classified with SVM and decision tree classifier for efficient recognition of VAs. Aim of the reduction in data size is to reduce the computational time. Our proposed method achieves high classification accuracy in hybrid-based features and by reducing the feature dimension, it reduces the computational complexity significantly. The accuracy of 99.83% and computational time of 4.82 s is achieved when considering all 24 features. In reduced feature set, an accuracy of 99.62% with a very less computational time of 2.71 s was obtained for decision tree classifier which indicates the importance of selecting the important features for classification of VAS. With superior classification accuracy and low computation complexity, this system can be utilized in clinical practice for the recognition of ventricular arrhythmias. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024.

关键词： Classifiers Decision support system ECG signals Hybrid features Sudden cardiac arrest

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Zero Watermarking Algorithm for Medical Image Based on Resnet50-DCT

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computers, Materials & Continua 2023年第4期75卷 293-309页

作者： Mingshuai Sheng Jingbing Li Uzair Aslam Bhatti Jing Liu Mengxing Huang Yen-Wei Chen School of Information and Communication Engineering Hainan UniversityHaikou570228China State Key Laboratory of Marine Resource Utilization in the South China Sea Hainan UniversityHaikou570228China School of Computer Science and Technology Hainan UniversityHaikou570228China Research Center for Healthcare Data Science Zhejiang LabHangzhou311121China Graduate School of Information Science and Engineering Ritsumeikan UniversityKyoto5258577Japan

Medical images are used as a diagnostic tool, so protecting theirconfidentiality has long been a topic of study. From this, we propose aResnet50-DCT-based zero watermarking algorithm for use with medicalimages. To begin, we use Resnet50, a pre-training network, to draw out thedeep features of medical images. Then the deep features are transformedby DCT transform and the perceptual hash function is used to generatethe feature vector. The original watermark is chaotic scrambled to get theencrypted watermark, and the watermark information is embedded into theoriginal medical image by XOR operation, and the logical key vector isobtained and saved at the same time. Similarly, the same feature extractionmethod is used to extract the deep features of the medical image to be testedand generate the feature vector. Later, the XOR operation is carried outbetween the feature vector and the logical key vector, and the encryptedwatermark is extracted and decrypted to get the restored watermark;thenormalized correlation coefficient (NC) of the original watermark and therestored watermark is calculated to determine the ownership and watermarkinformation of the medical image to be tested. After calculation, most ofthe NC values are greater than 0.50. The experimental results demonstratethe algorithm’s robustness, invisibility, and security, as well as its ability toaccurately extract watermark information. The algorithm also shows goodresistance to conventional attacks and geometric attacks.

关键词： Medical images deep residual network resnet50-DCT privacy protection robustness security

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Security Improvement for Deep Learning-Based Semantic Communication Systems

Security Improvement for Deep Learning-Based Semantic Commun...

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2024 International Conference on Advanced Technologies for Communications, ATC 2024

作者： Tan, Hung Ngo Luu Nguyen, Van-Dinh Huynh-The, Thien Nguyen, Toan-Van Vo, Phuong Luu School of Computer Science and Engineering International University-VNU HCMC Viet Nam Vietnam National University Ho Chi Minh City Viet Nam College of Engineering and Computer Science Center for Environmental Intelligence VinUniversity Hanoi Viet Nam Hcmc University of Technology and Education Department of Computer and Communications Engineering Viet Nam

ISBN: (纸本)9798350353983

Semantic communication is becoming increasingly popular for wireless image transmission due to its superior communication efficiency. However, current deep learning-based semantic systems designed for semantic communication, though efficient, remain vulnerable to eavesdropping and often overlook security measures at the physical layer. To address this issue, this paper presents a deep learning-based system with joint source-channel coding (JSCC) and cyclical consistent generative adversarial network that enhances the security of semantic communication systems. We also design a convolutional neural network for the encoder and decoder which is trained to extract and transmit semantic features while minimizing the risk of privacy leakage. The artificial noise at the physical layer is employed at the source to degrade the eavesdropping ability of the eavesdropper. We show through experiments that under the artificial noise strategy, the legitimate user achieves a higher structural similarity index measure (SSIM) and peak signal-to-noise ratio (PSNR) than the eavesdropper. Moreover, the semantic system with JSCC offers better SSIM and PSNR than the separated source and channel coding models while preserving the confidentiality of semantic information during wireless transmission. This enhanced security framework opens new opportunities for secure and reliable communication of semantic information in diverse applications. © 2024 IEEE.

关键词： Channel coding

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Dynamic prompting class distribution optimization for semi-supervised sound event detection

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Frontiers of Information Technology & Electronic engineering 2025年第4期26卷 556-567页

作者： Lijian GAO Qing ZHU Yaxin SHEN Qirong MAO Yongzhao ZHAN School of Computer Science and Communication Engineering Jiangsu UniversityZhenjiang 212016China Jiangsu Engineering Research Center of Big Data Ubiquitous Perception and Intelligent Agricultural Applications Zhenjiang 212016China

Semi-supervised sound event detection(SSED)tasks typically leverage a large amount of unlabeled and synthetic data to facilitate model generalization during training,reducing overfitting on a limited set of labeled ***,the generalization training process often encounters challenges from noisy interference introduced by pseudo-labels or domain knowledge *** alleviate noisy interference in class distribution learning,we propose an efficient semi-supervised class distribution learning method through dynamic prompt tuning,named prompting class distribution optimization(PADO).Specifically,when modeling real labeled data,PADO dynamically incorporates independent learnable prompt tokens to explore prior knowledge about the true ***,the prior knowledge serves as prompt information,dynamically interacting with the posterior noisy-class distribution *** this case,PADO achieves class distribution optimization while maintaining model generalization,leading to a significant improvement in the efficiency of class distribution *** with state-of-the-art methods on the SSED datasets from DCASE 2019,2020,and 2021 challenges,PADO achieves significant performance ***,it is readily extendable to other benchmark models.

关键词： Prompt tuning Class distribution learning Semi-supervised learning Sound event detection

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An accurate cell segmentation method for high-resolution complex brain image 9

An accurate cell segmentation method for high-resolution com...

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9th International Conference on Image, Vision and Computing, ICIVC 2024

作者： Xu, Xiaofeng Li, Shuai Zheng, Wei Henan Engineering Research Center of Intelligent Processing for Big Data of Digital Image School of Computer Science and Technology Nanyang Normal University Nanyang China

ISBN: (纸本)9798350385991

Cell segmentation is a key step in medical image analysis and cell biology research, and is important in areas such as understanding cell behaviour, cancer detection and cell cycle analysis. This task is challenging due to the presence of overlap between cells and image noise. With the development of imaging techniques, the amount and quality of acquired cellular image data have increased significantly and even contain diverse information such as neuronal structures and blood vessels, which puts higher demands on segmentation techniques. Real rat brain cell images obtained using micro-optical slice tomography (MOST) contain complex and diverse information such as neuronal structure, cell morphology, and the distribution of blood vessels, demonstrating the characteristics of real biological samples, for which segmentation of the target region is more difficult. In order to solve these challenges, this paper proposes a new cell image segmentation method that combines watershed order differential feature extraction and fuzzy entropy spatial theory. Due to the complexity of image information, we first use traditional image enhancement techniques. Then feature extraction is performed and fuzzy entropy processing is added to achieve accurate identification of cell boundaries. Then distance transform and curvature analysis are added to further segment the cell clusters into individual cells while avoiding over-segmentation and missed detection. Experiments demonstrate that the method not only shows high performance on our dataset, but also validates our method on several publicly available cellular datasets respectively, proving that the segmentation algorithm shows great potential for segmentation on different types of cells. Future research will further optimise the model to improve segmentation performance on complex and irregularly shaped cells and explore the application of these methods to other types of medical image segmentation tasks. © 2024 IEEE.

关键词： Image segmentation

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REMOTELY CONTROLLABLE DNA MICROFLOW 26

REMOTELY CONTROLLABLE DNA MICROFLOW

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26th International Conference on Miniaturized Systems for Chemistry and Life sciences, MicroTAS 2022

作者： Udono, Hirotake Nomura, Shin-Ichiro M. Takinoue, Masahiro Department of Computer Science School of Computing Tokyo Institute of Technology Japan Department of Robotics Graduate School of Engineering Tohoku University Japan

ISBN: (纸本)9781733419048

Despite an emerging interest in fluid manipulation within confined environment such as biological cells and micro-scale biomimics, remotely controllable micro-scale flow remains unexplored. However, intracellular microfluidics is not compatible with established top-down microfluidic methods because of limited accessibility. Here, we demonstrate photoresponsive DNA fluid, condensate of DNA nanostructures (DNA motifs), as a versatile mechanistic tool for the remote control of microflow. Insertion site of azobenzene, introduced for photocontrollability, determines an extent of dynamic shift in binding enthalpy between DNA motifs, providing the underpinning mechanism for drastic and facile flow-mode switching. © 2022 MicroTAS 2022 - 26th International Conference on Miniaturized Systems for Chemistry and Life sciences. All rights reserved.

关键词： Remote control

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