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Focal Modulation Based End-to-End Multi-Label Classification for Chest X-Ray image Classification

Focal Modulation Based End-to-End Multi-Label Classification...

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IEEE Signal processing and communications Applications (SIU)

作者： Şaban Öztürk Tolga Çukur Elektrik ve Elektronik Mühendisligi Bölümü Bilkent Üniversitesi Ankara Türkiye Elektrik ve Elektronik Mühendisligi Bölümü Amasya Üniversitesi Amasya Türkiye Ulusal Manyetik Rezonans Araştırma Merkezi (UMRAM) Bilkent Üniversitesi Ankara Türkiye

Chest X-ray imaging is of critical importance in order to effectively diagnose chest diseases, which are increasing today due to various environmental and hereditary factors. Although chest X-ray is the most commonly used device for detecting pathological abnormalities, it can be quite challenging for specialists due to misleading locations and sizes of pathological abnormalities, visual similarities, and complex backgrounds. Traditional deep learning (DL) architectures fall short due to relatively small areas of pathological abnormalities and similarities between diseased and healthy areas. In addition, DL structures with standard classification approaches are not ideal for dealing with problems involving multiple diseases. In order to overcome the aforementioned problems, firstly, background-independent feature maps were created using a conventional convolutional neural network (CNN). Then, the relationships between objects in the feature maps are made suitable for multi-label classification tasks using the focal modulation network (FMA), an innovative attention module that is more effective than the self-attention approach. Experiments using a Chest x-ray dataset containing both single and multiple labels for a total of 14 different diseases show that the proposed approach can provide superior performance for multi-label datasets.

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Infrared image Noise Reduction and Edge Enhancement Algorithm Based on Local Intuitionistic Fuzzy Entropy

Infrared Image Noise Reduction and Edge Enhancement Algorith...

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Networking, communications and Information Technology (NetCIT), International conference on

作者： Yihan Niu Lanzhou Resources&Environment Voc-Tech University Lanzhou Gansu China

Aiming at the deficiency that the traditional P-M diffusion model can't distinguish the flat area and the detailed area with similar gradient changes, a new diffusion model is proposed. In image engineering, the actual image is flawed. On the one hand, due to the imperfection of image acquisition equipment, transmission equipment and receiving equipment, the image will be disturbed by various noises in the process of acquisition, transmission and storage, and the image will become blurred, resulting in incomplete image information. On the other hand, due to the limitation of various image processing algorithms, some negative effects will be brought. Firstly, the optimal threshold of infrared image is obtained by using the fuzzy maximum straightness criterion. On this basis, three spatial domain enhancement algorithms are proposed, including nonlinear stretching algorithm, improved plateau histogram equalization algorithm and double closed-value adaptive piecewise linear transformation algorithm for the target and background area. Experiments show that their processing effects have their own advantages. Then, with the help of wavelet theory, this paper proposes an infrared image contrast enhancement algorithm based on fuzzy logic and wavelet transform in the frequency domain. The algorithm can not only significantly improve the contrast of the image, but also have a better noise reduction effect. The experimental results show that, compared with the traditional model, the new model keeps the details more completely, removes the noise more cleanly, and has excellent visual and quantitative effects.

关键词： Wavelet transforms Fuzzy logic visualization Histograms Wavelet domain image processing image edge detection

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Copy-Move image Forgery Detection Using Discrete Wavelet Transform 3rd

Copy-Move Image Forgery Detection Using Discrete Wavelet Tra...

引用

3rd International conference on Recent Trends in image processing and Pattern Recognition, RTIP2R 2020

作者： Mahale, Vivek Yannawar, Pravin Gaikwad, Ashok Institute of Management Studies and Information Technology Aurangabad Maharashtra431001 India Vison and Intelligent System Lab Department of CS and IT Dr. Babasaheb Ambedkar Marathwada University Aurangabad Maharashtra India

ISBN: (纸本)9789811605062

To expressing visual information Digital images play a significant role. On social media this information in form of digital image is continually increasing. Now a day easily digital images are misrepresented by peoples because availability of software using for image processing. In this paper we represent novel approaches which identify Copy-Move forgery in image using DWT. First we read image and convert this image into grayscale image. Then apply wavelet transform. Extract overlapped high contrast blocks of the coarsest level. Then lexicographically sorting and calculate phase correlation between rows. Extract blocks from reference and match region. Then draw the duplicate region. For evaluation of our system we calculate TPR, FPR, Accuracy and AUC. Our system accuracy 94.58%. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Discrete wavelet transforms

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Security of images Using DNA Encryption and Lorenz System in Cloud Storage 7th

Security of Images Using DNA Encryption and Lorenz System in...

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7th International conference on Soft Computing and Signal processing, ICSCSP 2024

作者： Gupta, Raghav Gullapalli, Manasa Suseela, G. Department of Networking and Communications School of Computing Science and Engineering SRM Institute of Science and Technology Tamil Nadu Kattankulathur603203 India Department of Networking and Communications Faculty of Engineering and Technology School of Computing Science and Engineering SRM Institute of Science and Technology Tamil Nadu Kattankulathur603203 India

ISBN: (纸本)9789819799251

images are the most valuable yet susceptible type of data since they may be used to track user activity, exploited by hackers, and utilized for fraud and identity theft. Storing images in local storage, on a device’s disk, within a web browser’s local storage, or on a server’s file system can introduce several vulnerabilities and risks, particularly regarding security and data integrity. Using image encryption as a security solution provides a reliable means of protecting sensitive visual data from unauthorized access and potential security threats. The convenience of cloud-based systems, however, comes with the responsibility of protecting data against cyber threats. The widespread usage of cloud storage has led to concerns about the security of images stored there. Addressing the challenges associated with securing images in the cloud, this project brings a novel approach that blends cryptographic techniques, such as DNA encryption, with the dynamism of chaotic systems like the Lorenz system. Such integrative methodologies aim to meet current security demands for image storage and transmission in the digital age. This dual-layer encryption approach ensures that images encrypted and stored in cloud storage are safeguarded with a security measure that is currently unparalleled, making unauthorized access and decryption practically impossible without the specific, uniquely generated decryption key. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Sensitive data

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Fully Neural Network Mode Based Intra Prediction of Variable Block Size

Fully Neural Network Mode Based Intra Prediction of Variable...

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IEEE International conference on visual communications and image processing (VCIP)

作者： Sun, Heming Yu, Lu Katto, Jiro Waseda Univ Waseda Res Inst Sci & Engn Tokyo Japan JST PRESTO 4-1-8 Honcho Kawaguchi Saitama Japan Zhejiang Univ Inst Informat & Commun Engn Hangzhou Peoples R China Waseda Univ Dept Comp Sci & Commun Engn Tokyo Japan

ISBN: (纸本)9781728180687

Intra prediction is an essential component in the image coding. This paper gives an intra prediction framework completely based on neural network modes (NM). Each NM can be regarded as a regression from the neighboring reference blocks to the current coding block. (1) For variable block size, we utilize different network structures. For small blocks 4x4 and 8x8, fully connected networks are used, while for large blocks 16x16 and 32x32, convolutional neural networks are exploited. (2) For each prediction mode, we develop a specific pre-trained network to boost the regression accuracy. When integrating into HEVC test model, we can save 3.55%, 3.03% and 3.27% BD-rate for Y, U, V components compared with the anchor. As far as we know, this is the first work to explore a fully NM based framework for intra prediction, and we reach a better coding gain with a lower complexity compared with the previous work.

关键词： Intra prediction image compression deep learning fully connected layer convolutional neural network

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Diabetic Retinopathy Detection Using CNN With Res-LSTMDN

Diabetic Retinopathy Detection Using CNN With Res-LSTMDN

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International conference on communications and Signal processing

作者： R. Gayathri S Karthikeyan T KausheekRaj S Keerthana Department of Electronics and Communication Rajalakshmi Engineering College Chennai India

ISBN: (数字)9798350353068

ISBN: (纸本)9798350353075

For diabetic patients, early diagnosis and treatment of diabetic retinopathy (DR) are essential since they can avert irreversible visual loss. The ways we currently find DR aren't very accurate or fast, so we need better ways to do it automatically. CNNs, a type of machine learning algorithm, have been shown to work well for analyzing medical images and could be a good option for detecting DR. Among CNN architectures, ResNet-152 has gained reputation for its depth and effectiveness in image classification tasks. However, to further enhance its performance in DR detection, this study does a modification to the final layer of ResNet-152. Specifically, the final layer is replaced with Long-Short Term Memory (LSTM) and Deep Neural Network (DNN) components, resulting in an ensemble approach termed Res-LSTMDN. This novel architecture aims to improve the hierarchical feature learning of ResNet-152 while integrating the sequential memory capabilities of LSTM and the flexibility of DNN. Res-LSTMDN uses advanced technology to study eye images, specifically those from people with diabetes. By finding unique patterns in these images, this technique aims to improve the accuracy of detecting diabetes-related eye problems. This will allow for early intervention, potentially preventing vision loss. As part of the ongoing efforts to develop advanced computer tools for diabetes diagnosis, Res-LSTMDN has the potential to significantly impact global diabetes management strategies.

关键词： Representation learning Diabetic retinopathy visualization Machine learning algorithms Memory management Signal processing Convolutional neural networks

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Accurate Recognition of Kiwifruit Based on Improved YOLOv5

Accurate Recognition of Kiwifruit Based on Improved YOLOv5

引用

Natural Language processing (ICNLP), International conference on

作者： Sun Wei Sun Yi Jun Li Zhao Chen Guo Jing School of Communications and Information Engineering Xi’an University of Posts and Telecommunications Xi’an China

In order to meet the urgent needs of automation and intelligent picking of kiwifruit, aiming at the problems of unreasonable construction of kiwifruit data set, low fruit recognition accuracy and poor spatial positioning in the natural environment of orchard, a precise recognition and visual positioning method of kiwifruit based on improved Yolov5s was proposed. In view of the growth characteristics of kiwifruit in trellis orchards, a multi-type kiwifruit data set was first constructed. Furthermore, the attention mechanism and multi-scale module are combined to improve the Yolov5s network structure, identify kiwifruit and extract the center coordinates of the prediction box. The experimental results show that the average accuracy of the model for six kiwifruit types under different weather and light conditions is 98 %. The single image recognition time of $1280\times 720$ pixel is about 13.8 ms, and the weight is only 15.21 Mb. It can be seen that this study can provide technical support for the vision system of kiwifruit automatic picking robot, and provide reference for the intelligent recognition and positioning of other fruits (such as apples, mangoes and oranges).

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Localization of Eye Region in Infrared Thermal images using Deep Neural Network 6

Localization of Eye Region in Infrared Thermal Images using ...

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6th International conference on Wireless communications, Signal processing and Networking (IEEE WiSPNET)

作者： Meenakshi, Madura R. Padmapriya, N. Venkateswaran, N. Ravikumar, R. Chelliah, Ramya Sri Sivasubramaniya Nadar Coll Engn Dept Math Kalavakkam Tamil Nadu India Tagore Med Coll Hosp Dept Ophthalmol Chennai Tamil Nadu India

ISBN: (纸本)9781665440868

Infrared (IR) thermal images are produced by recording the thermal radiation emitted from the object. It has been increasingly used in industrial installations, surveillance, evaluating human health, etc. Medical IR thermal images are used as a tool for early diagnosis of certain diseases in the human beings. It can detect the heat patterns of the body as well as irregular blood flow. Compared to visual images, thermal images have low resolution and low contrast. Hence in IR thermal images eye detection is more challenging, for ocular surface disease diagnosis. This article proposes an automatic eye localization method from IR thermal images using YOLO v2 object detector. The performance of eye localization in IR thermal images using YOLO v2 shows mean average precision 97% and mean intersection over union (IoU) 90% for test images.

关键词： Infrared thermal image Region of interest Eye localization YOLO v2 Deep neural network

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On Pre-chewing Compression Degradation for Learned Video Compression

On Pre-chewing Compression Degradation for Learned Video Com...

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IEEE visual communications and image processing (VCIP)

作者： Man Minh Ho Heming Sun Zhiqiang Zhang Jinjia Zhou Hosei University Tokyo Japan Waseda University and JST PRESTO Tokyo Japan

ISBN: (纸本)9781665475938

Artificial Intelligence (AI) needs huge amounts of data, and so does Learned Restoration for Video Compression. There are two main problems regarding training data. 1) Preparing training compression degradation using a video codec (e.g., Versatile Video Coding - VVC) costs a considerable resource. Significantly, the more Quantization Parameters (QPs) we compress with, the more coding time and storage are required. 2) The common way of training a newly initialized Restoration Network on pure compression degradation at the beginning is not effective. To solve these problems, we propose a Degradation Network to pre-chew (generalize and learn to synthesize) the real compression degradation, then present a hybrid training scheme that allows a Restoration Network to be trained on unlimited videos without compression. Concretely, we propose a QP-wise Degradation Network to learn how to compress video frames like VVC in real-time and can transform the degradation output between QPs linearly. The real compression degradation is thus pre-chewed as our Degradation Network can synthesize the more generalized degradation for a newly initialized Restoration Network to learn easier. To diversify training video content without compression and avoid overfitting, we design a Training Framework for Semi-Compression Degradation (TF-SCD) to train our model on many fake compressed videos together with real compressed videos. As a result, the Restoration Network can quickly jump to the near-best optimum at the beginning of training, proving our promising scheme of using pre-chewed data for the very first steps of training. In other words, a newly initialized Learned Video Compression can be warmed up efficiently but effectively with our pre-trained Degradation Network. Besides, our proposed TF-SCD can further enhance the restoration performance in a specific range of QPs and provide a better generalization about QPs compared with the common way of training a restoration model. Our work is

关键词： Degradation Training image coding visual communication Transforms Streaming media Video compression

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Single image Dehazing Using CycleGAN Based on Feature Fusion

Single Image Dehazing Using CycleGAN Based on Feature Fusion

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Chinese Control and Decision conference, CCDC

作者： Xiaofei Jin Dengyin Zhang Songhao Lu Dingxu Guo Wenye Ni Xu Li School of Communications and Information Engineering Nanjing University of Posts and Telecommunications Nanjing China School of Internet of Things Nanjing University of Posts and Telecommunications Nanjing China

Due to the difficulty of obtaining paired data sets from the real world to train the network, most of the current dehazing networks are trained by synthetic hazy data sets, which will have drawbacks such as poor generalization ability to natural haze scenes and loss of depth details. This paper proposes an image dehazing method using CycleGAN based on improved feature fusion to solve the problem. The method is designed with an encoder-decoder structure in the generator network, enabling more feature information to be extracted at multiple scales. In order to restore the detailed information of the image, this paper introduces the residual dense block instead of the convolution module to extract and fuse the feature information under different receptive fields in each stage of the network. Aiming at the complexity of the fog distribution in the actual scene, this paper introduces an improved channel and spatial attention mechanism in the skip connection of the network to accomplish non-uniform processing of haze areas with different concentrations. At the same time, to improve the quality of the generated image, this paper introduces perceptual loss to enhance the detailed information of the output features, making the generated image more realistic. The experimental findings suggest that the proposed method can achieve better subjective visual effects and image details, and the outcomes of objective indicators are also improved.

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