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DenseNet 121 Framework For Automatic Feature Extraction Of Diabetic Retinopathy images 4

DenseNet 121 Framework For Automatic Feature Extraction Of D...

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4th international conference on Emerging Systems and Intelligent Computing, ESIC 2024

作者： Gokul Siddarth, S. Chokkalingam, Sp Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Chennai601103 India

ISBN: (纸本)9798350349856

Automatic feature extraction from medical images is one of the most important aspects of early disease detection and treatment. this work presents a novel method for autonomously extracting features from diabetic retinopathy images using the DenseNet-121 framework. Diabetes frequently has a side effect called diabetic retinopathy, which requires early detection to protect vision and enhance patient outcomes. Typical methodologies for feature extraction from medical photographs usually struggle to capture the intricate details and patterns shown in diabetic retinopathy imagery. To solve these challenges and facilitate a more comprehensive analysis of retinopathy images, we propose to use the widely recognized DenseNet-121 architecture, which is well known for its deep feature extraction and feature reuse capabilities. the test results on a large sample of diabetic retinopathy images validate the. © 2024 IEEE.

关键词： Feature extraction

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A Deep Neural Network-Based Segmentation Method for Multimodal Brain Tumor images 13th

A Deep Neural Network-Based Segmentation Method for Multimod...

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13th IFIP TC 12 international conference on Intelligent Information Processing, IIP 2024

作者： Meng, Zuqiang Peng, Yue School of Computer and Electronic Information Guangxi University Nanning530004 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China

ISBN: (纸本)9783031579189

Medical image segmentation plays an important role in medical diagnosis. Accurate segmentations of brain tumor images require well-designed segmentation models and sufficient high-quality well-labeled training samples, but it is difficult for existing segmentation methods to meet these requirements. In this paper, we propose a segmentation method, which involves a GAN-nested model and an improved UNet. the GAN-nested model is used to automatically generate sufficient well-labeled brain tumor images, which are used as training samples;the improved UNet is good at extracting the detailed features of brain tumor images, and therefore it can conduct the accurate segmentation for brain tumor images using high-quality training samples generated by the GAN-nested model. Extensive experimental results prove that the proposed method is effective and obtains state-of-the-art performance on the given datasets. © IFIP international Federation for Information Processing 2024.

关键词： Diagnosis

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Spatio-Temporal Graph Neural Networks for Accurate Crime Prediction 13

Spatio-Temporal Graph Neural Networks for Accurate Crime Pre...

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13th international conference on computer and Knowledge Engineering, ICCKE 2023

作者： Roshankar, Rojan Keyvanpour, Mohammad Reza Alzahra University Data Mining Laboratory Department of Computer Engineering Faculty of Engineering Tehran Iran Alzahra University Department of Computer Engineering Faculty of Engineering Tehran Iran

ISBN: (纸本)9798350330151

As a matter of public safety and resource allocation, crime prediction is of paramount importance. As a result of applying data preprocessing techniques and a graph-based approach, this paper presents a crime prediction model. this study shows a comprehensive analysis of crime incidents reported in Chicago over five years, utilizing several preprocessing steps in preparing the dataset. Irrelevant features are eliminated, missing values are handled, and a new feature is extracted. An algorithm based on the k-nearest neighbors method is proposed for representing crime incidents by constructing a graph representation. Each crime incident is connected to its k nearest neighbors based on spatial coordinates. this study compares graph-based machine learning algorithms and conventional approaches, including logistic regression, decision trees, and the recently related work, Spatial-Temporal Meta-path Guided Explainable Crime Prediction (STMEC). the results reveal that the graph-based model demonstrates superior accuracy, recall, and F1 scores compared to traditional models, notably outperforming the recently related work, STMEC. Based on the current study, the graph-based approach effectively captures crime data's spatial dependencies and patterns. Incorporating additional features and fine-tuning hyperparameters of the model can provide valuable insights into crime prevention strategies and urban planning applications. © 2023 IEEE.

关键词： Forecasting

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Generating Caption From images Using Flickr Image Dataset 15

Generating Caption From Images Using Flickr Image Dataset

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15th international conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Sharma, Piyush Kaur, Kiranpreet Gandotra, Aaryan Patial, Pranay Radhika Gaba, Sehaj Chandigarh University Department of Cse Mohali India

ISBN: (纸本)9798350370249

In an age where the Internet is dominated by visual content, the generation of animated captions has become a must. It has always been an interesting study for researchers in the Department of Artificial Intelligence. Enabling the machine to describe images with the same skillful accuracy as the human has important applications in various fields such as robotic vision, manufacturing, and beyond this project integrates recurrent neural networks with is a topic of contextual parallelism dedicated to extracting features from images Natural Language Processing computer Vision and integrating them seamlessly, this research provides insights a it goes further on this interdisciplinary topic. Additionally, annotations for the sample images are created and performed a comparative analysis of different feature extraction and encoder patterns to determine which model provided the highest accuracy and delivered the desired results. © 2024 IEEE.

关键词： Caption from an image Caption of an image Encoder- Decoder outputs text from an image

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Artificial Intelligence Assistive Fire Detection and Seeing the Invisible through Smoke Using Hyperspectral and Multi-spectral images 13

Artificial Intelligence Assistive Fire Detection and Seeing ...

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13th IEEE international conference on Electronics and Information Technologies, ELIT 2023

作者： Alboody, Ahed Qaisar, Saeed Mian Roussel, Gilles CESI-LINEACT Nice06200 France CESI-LINEACT Lyon69100 France Electrical and Computer Engineering Department Effat University Jeddah22332 Saudi Arabia Laboratoire LISIC Université du Littoral Côte d’Opale Calais62228 France

ISBN: (纸本)9798350383096

the global warming has serious impact on our climate. Due to this, the frequency and the intensity of forest fires is increasing. It has shown serious challenges such as the protection of resources, human and wild life, health, and property. this study focuses on developing an artificial intelligence assistive innovative solution for active fire detection in the context of smart cities and vicinities. this paper addresses spectral analysis, detection and classification of active fires and seeing the invisible through smoke and thin clouds. the appealing applications are in urban surveillance, smart cities, future industries, forests and earth observation. the idea is realizable by using an intelligent hybridization of machine/deep learning models and using multi-sensor images (aerial, satellite). For this purpose, we use hyperspectral images (Visible, Near Infra-red (NIR) and Short-Wave Infrared (SWIR)) from AVIRIS aerial and Multi-Spectral Sentinel-2 satellite images. AVIRIS images are 224 spectral bands of wavelengths with a spatial resolution of 15 meters, which varies from 366nm (nanometers) up to 2500nm. However, AVIRIS image studied for their spectral richness of wavelengths not yet completely exploited by machine and deep learning and in SWIR to detect active fires. While, Sentinel-2 image has 13 spectral bands (Visible, NIR and SWIR) with three spatial resolutions (10, 20 and 60 meters). First, we explain and describe the preparation phase of hyperspectral and multispectral image databases of forest fires. these databases contain hyperspectral and multispectral endmembers data of different sites for forest fires. then, we conduct a spectral analysis from these endmembers to characterize the hyperspectral/multispectral reflectance of active fires to identify the distinct wavelengths for fire detection. We identify the wavelengths that can be used for an effective identification of fire and to see through fires smoke and thin clouds. Onward, the selected featu

关键词： Smoke

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Research on Digital Identification for Cultural Protection of Yao People Clothing patterns in Northern Guangdong 16th

Research on Digital Identification for Cultural Protection o...

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16th international conference on Cross-Cultural Design (CCD) Held as Part of the 26th international conference on Human-computer Interaction (HCII)

作者： Zhang, Yao Yang, Zhi Beijing Inst Technol Zhuhai Peoples R China Natl Cheng Kung Univ Inst Creat Ind Design Tainan Taiwan Wuhan Inst Design & Sci Wuhan Peoples R China

ISBN: (纸本)9783031609039;9783031609046

patterns serve as significant cultural and artistic symbols among the Yao people in northern Guangdong, China. they play a crucial role in clothing art, embodying national culture and historical heritage. this research adopts various research methods, including literature research, case studies, and field investigations, to systematically identify patterns found in the traditional costumes of the Yao people in northernGuangdong, China. Additionally, it utilizes semi-structured interviews to analyze, organize, and categorize these patterns within the cultural context of the Yao people in this region. To enhance the understanding of these patterns, digital technology is used to draw and extract vector graphics of the patterns, followed by a comprehensive analysis of their units, color combinations, and compositional features. Furthermore, this study delves into the characteristics, origins, and cultural symbolism behind these patterns. this research aims to improve the digital identification of Yao costume patterns in northern Guangdong, China. the software was also used to extract typical pattern units, and through the combination of basic unit patterns, transform and design a modern pattern series with the style of the Yao people in northern Guangdong. the results of this study are propitious to the protection, digital identification and inheritance of the outstanding traditional pattern culture of the Yao nationality in northern Guangdong, and have high practical value to the future research on the modern digital application of the traditional pattern of theYao nationality in northernGuangdong.

关键词： Yao people in northern Guangdong Clothing patterns Culture Digital identification Protection Application

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Enhancing Pneumonia Detection using Vision Transformer with Dynamic Mapping Re-Attention Mechanism 13

Enhancing Pneumonia Detection using Vision Transformer with ...

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13th international conference on computer and Knowledge Engineering, ICCKE 2023

作者： Labbaf Khaniki, Mohammad Ali Mirzaeibonehkhater, Marzieh Manthouri, Mohammad K.N. Toosi University of Technology Department of Control Engineering Tehran Iran Indiana University-Purdue University Department of Electrical and Computer Engineering United States Shahed University Department of Electrical and Electronic Engineering Tehran Iran

ISBN: (纸本)9798350330151

Pneumonia is a prevalent respiratory infection with potentially life-threatening consequences. In this research, we propose a novel deep learning approach to enhance pneumonia detection using the Vision Transformer (ViT) architecture with a dynamic mapping re-attention mechanism. the re-attention mechanism in the ViT architecture facilitates the flow of information between patches of the input image, enabling contextual understanding through self-attention. this mechanism captures long-range dependencies and enhances the ViT's ability to effectively recognize and interpret images. the dynamic mapping attention mechanism allows the model to dynamically assign attention weights to different regions of the input data based on their relevance to pneumonia detection. By adaptively adjusting the attention weights, the model can capture both local details and global dependencies, enabling more accurate diagnosis. the ViT utilizes the dynamic mapping re-attention mechanism with attention dropout. Here the performance of this ViT compared to the conventional ViT and the ViT incorporating the re-attention mechanism. this comparative analysis offers valuable insights into the effectiveness of the proposed dynamic mapping re-attention mechanism. Additionally, it highlights the added benefits of attention dropout in enhancing the overall performance of the model. © 2023 IEEE.

关键词： Image enhancement

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Comparative analysis of Neonatal Facial Expression from images using Deep Learning 15

Comparative Analysis of Neonatal Facial Expression from Imag...

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15th international conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Das, Chirantan Nanda, Dibyashree Chatterjee, Sudip Dutta, Dipankar University Institute of Technology Burdwan University Department of Computer Science and Engineering Burdwan India

ISBN: (纸本)9798350370249

Understanding and interpreting infant emotions is crucial for early childhood development and effective caregiving. In this work, we implemented various deep learning (DL) models to detect emotions in infants from images. By leveraging advancements in computer vision and DL techniques, our goal is to develop a comprehensive system capable of accurately recognizing and classifying a wide range of emotional states in infants. We analyze facial expressions in images of infants, utilizing DL models to extract relevant features and identify emotional patterns. Our approach is evaluated using a comprehensive dataset of infants exhibiting various emotional states. through rigorous experimentation, we assess different feature extraction methods, machine learning (ML) algorithms, and deep convolutional neural networks (CNNs), measuring performance using accuracy metrics to determine the system's effectiveness in identifying and classifying infant emotions. © 2024 IEEE.

关键词： CNN computer vision Deep Learning (DL) Infant emotions Multi-stream Fusion Neonatal CNN (NCNN) Squeeze and Excitation Block

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Defocus Blur Synthesis and Deblurring via Interpolation and Extrapolation in Latent Space 20th

Defocus Blur Synthesis and Deblurring via Interpolation and ...

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20th international conference on computer analysis of images and patterns (CAIP)

作者： Mazilu, Ioana Wang, Shunxin Dummer, Sven Veldhuis, Raymond Brune, Christoph Strisciuglio, Nicola Univ Twente Enschede Netherlands

ISBN: (纸本)9783031442391;9783031442407

though modern microscopes have an autofocusing system to ensure optimal focus, out-of-focus images can still occur when cells within the medium are not all in the same focal plane, affecting the image quality for medical diagnosis and analysis of diseases. We propose a method that can deblur images as well as synthesize defocus blur. We train autoencoders with implicit and explicit regularization techniques to enforce linearity relations among the representations of different blur levels in the latent space. this allows for the exploration of different blur levels of an object by linearly interpolating/extrapolating the latent representations of images taken at different focal planes. Compared to existing works, we use a simple architecture to synthesize images with flexible blur levels, leveraging the linear latent space. Our regularized autoencoders can effectively mimic blur and deblur, increasing data variety as a data augmentation technique and improving the quality of microscopic images, which would be beneficial for further processing and analysis. the code is available at https://***/nis-research/linear-latent-blur.

关键词： Microscope images Deblurring Defocus blur synthesis Regularized autoencoders

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Image Segmentation Network Based on Convolutional Layers, a Clustering Layer and Cluster Classification 37th

Image Segmentation Network Based on Convolutional Layers, a ...

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37th international conference on computer Applications in Industry and Engineering

作者： Rosas-Romero, Roberto Univ Amer Puebla San Andres Cholula Puebl Mexico

ISBN: (纸本)9783031762727;9783031762734

this work contributes to the detection of regions of interest on images and their corresponding classification in medical imaging applications by introducing an image segmentation network that consists of four stages. In the first stage, multi-resolution processing is applied to outline regions where further segmentation and classification are to be conducted. Subsequently, a quad-tree division stage followed by a clustering stage deliver an image that is divided into unlabeled clusters. the output stage assigns each cluster to one class. this architecture offers flexibility in the input and output stages since this network can be fed with images of any size and the output stage can be implemented with any traditional classification model such as k-nearest neighbors, multi-layer perceptron, and support vector machine. Another contribution of this work is that this network does not rely on a very large number of annotated images for its training.

关键词： Image segmentation Convolutional Neural Networks Multi-Resolution analysis Clustering

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