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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 ...

引用

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...

引用

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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An Improved Ultra-High Definition Panoramic Video Mosaic Method for Airport Scene 8

An Improved Ultra-High Definition Panoramic Video Mosaic Met...

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8th IEEE International conference on Computer and communications, ICCC 2022

作者： Wang, Kai Yang, Tao Zhang, Xiaohai Tang, Mozhen The 2nd Research Institute Civil Aviation Administration of China Chengdu China Beijing Capital International Airport Co. Ltd Beijing China

ISBN: (纸本)9781665450515

In the airport flight area video surveillance system, in order to solve the problems of low image resolution, mosaic fusion with ghosting and high processing time in multi-channel 4K High Definition (HD) video image stitching, an improved ultra-high definition panoramic video mosaic method for airport scene is proposed. Firstly, Improved Harris algorithm is used to extract the feature point of each image and the Euclidean distance is used to match feature vector. In addition, Gaussian feature of nonlinear weighted mask is calculated disposable and two-level of Gaussian pyramid is used for smoothing, besides, the original image and the smoothed image are directly used for the accumulation and difference. At the same time, because the overlapping part is divided into regions similar to Gaussian scale smoothing and Gaussian smoothing of mask, the transition effect is more natural and smooth during image mosaic and fusion. Finally, video image mosaic and fusion is realized and panoramic video image resolution can be up to 8K (ultra-high definition). The experiment at typical airport shows that the improved method meets the high-quality efficiency of visible video image stitching and fusion. © 2022 IEEE.

关键词： image resolution

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Smart Sorting: Enhancing Almond Quality with DenseNet-201-Based image Classification

Smart Sorting: Enhancing Almond Quality with DenseNet-201-Ba...

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International conference on Information Science and communications Technologies (ICISCT)

作者： Arpanpreet Kaur Amit Kumar Mishra Bura Vijay Kumar Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Computer Science and Engineering Graphic Era Hill University Dehradun Graphic Era Deemed to be University Dehradun Uttarakhand India School of Computer Science and Artificial Intelligence SR University Warangal India

ISBN: (数字)9798331527921

ISBN: (纸本)9798331527938

Considered as two varieties: damaged and undamaged, almonds are a rather consumed agricultural product. Maintaining consumer satisfaction and industry standards depends on good detection of damaged almonds., which could be influenced by mechanical damage., mold, or pests. On the other hand, slow, labor-intensive hand inspection methods prone to mistake demand automated classification systems. This work meets that demand by automating the categorization process using DenseNet-121, a convolutional neural network (CNN), therefore lowering labor expenses and increasing accuracy. Particularly considering the tiny dataset of 1,000 photos split equally between damaged and undamaged almonds, DenseNet-121's architecture-with its dense connections and capacity to reuse features-is well-suited for the almond classification challenge. Pre-trained on imageNet and polished for this particular purpose, the model-trained across 60 epochs using a 32-batch size-achieved an accuracy of 95.27%. Random horizontal flips, resizing., and center cropping among other data augmentation techniques improved the model's generalizing capability. This work demonstrates the model's ability to substitute human methods in actual environments by showing how well it employs little visual information to classify almonds. Automating almond sorting speeds and provides higher consistency in quality control as well as efficiency of sorting. This work opens the road for further research of machine learning techniques in agricultural processing with possible applications in food quality assurance.

关键词： Accuracy Quality assurance Process control Quality control Machine learning Data augmentation Data models Convolutional neural networks Sorting image classification

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visual Data Enciphering via DNA Encoding, S-Box, and Tent Mapping

Visual Data Enciphering via DNA Encoding, S-Box, and Tent Ma...

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International image processing, Applications and Systems conference (IPAS)

作者： Mohamed Gabr Hana Younis Marwa Ibrahim Sara Alajmy Wassim Alexan Faculty of MET The German University in Cairo Cairo Egypt Faculty of IET The German University in Cairo Cairo Egypt

ISBN: (纸本)9781665462204

The ever-evolving nature of the Internet and wireless communications, as well as the production of huge amounts of multimedia every day has created a dire need for their security. In this paper, an image encryption technique that is based on 3 stages is proposed. The first stage makes use of DNA encoding. The second stage proposed and utilizes a novel S-box that is based on the Mersenne Twister and a linear descent algorithm. The third stage employs the Tent chaotic map. The computed performance evaluation metrics exhibit a high level of achieved security.

关键词： Wireless communication Performance evaluation visualization image coding Chaotic communication DNA Production

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The Potential Utility of image Descriptions: User Identity Linkage across Social Networks Based on MultiModal Self-Attention Fusion

The Potential Utility of Image Descriptions: User Identity L...

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IEEE International conference on Performance, Computing and communications (IPCCC)

作者： Yunfeng Li Gaopeng Gou Gang Xiong Zhen Li Mingxin Cui Chinese Academy of Sciences Institute of Information Engineering School of Cyber Security University of Chinese Academy of Sciences

The task of user identity linkage across social networks aims to predict whether users from different social networks refer to the same person. This task plays a crucial role in cross-social network information dissemination and intelligent recommendations. However, existing user identity linkage tasks suffer from several challenges: 1) excessive reliance on social network topology, neglecting users’ visual modality information; 2) inadequate handling of noise in user feature data; and 3) ineffective fusion of users’ multimodal information. To address these issues, we investigated a method that utilizes heterogeneous multimodal posts, including user-generated text, images, and check-in messages, to achieve user identity linkage across social networks. We innovatively leveraged a pre-trained model for image-to-text conversion to further explore users’ image data and proposed an adversarial learning model based on the multimodal self-attention mechanism (AMSA). The AMSA model consists of four components: user feature extraction, user feature processing, user feature fusion, and adversarial learning. Specifically, AMSA initially employed advanced pre-trained models to extract features from multiple modalities of users, including images and text. Subsequently, it utilized multiple mechanisms, such as multi-head self-attention, to process data from each modality separately and then fused them into user representation vectors. Finally, AMSA employed adversarial learning to enhance the model’s learning capacity and mitigate semantic disparities in user information across different platforms. We conducted model performance evaluations on publicly available datasets, and experimental results demonstrated the superiority of the proposed AMSA model.

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A Garment Suggestion Engine Based on visual Similarity 28

A Garment Suggestion Engine Based on Visual Similarity

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28th Signal processing and communications Applications conference (SIU)

作者： Gokturk, Mehmet Kabakli, Bilal Yogurtcuglu, Hamza Gebze Tech Univ Dept Comp Sci Gebze Turkey Modanisa AS R&D Ctr Istanbul Turkey

ISBN: (纸本)9781728172064

In this study, a product suggestion engine has been developed for an e-commerce site portfolio focusing on garment items. Traditional collaborative filtering methods usually lack applicability due to high turnover rates in product lists. Thus, by focusing on visual similarity using deep leraning technique successful results were obtained and it has been concluded that application of this technique to real live e-commerce garment site will be suitable.

关键词： deep learning e-commerce image processing

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