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Histopathological Cancer Detection: A Comparison Study of Different Convolutional Neural Networks 2

Histopathological Cancer Detection: A Comparison Study of Di...

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2nd International conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC)

作者： Lyu, Zhuoyang Purdue Univ Coll Sci W Lafayette IN 47906 USA

ISBN: (纸本)9781510655201;9781510655195

The histopathological method is important for cancer diagnosis. The standard histopathological test today is extremely time-consuming, expensive, and labor-intensitive. However, as image digitalization technology became ubiquitous today, automatic analysis using computer-aided diagnosis tools provides a possible alternative. Using deep learning, especially for convolutional neural networks, it is possible to obtain results that have comparable performance to pathologists with a higher diagnosis speed. In this paper, seven different CNN architectures were trained on the Histopathological Cancer Detection dataset. The dataset contains 20000 images extracted from histopathological scans of lymph node sections. The results showed that there is a correlation between the performance and the architecture design's appropriateness on a specific dataset. DenseNet achieved the best result on this dataset with an accuracy of 0.9182 without data augmentation, and 0.9268 with data augmentation. One possible reason for the superior performance of DenseNet is that DenseNet mimics the diagnosis process of pathologists by integrating image features with multiple different scales. The experiment results suggest that DenseNet can serve as an effective tool to speed up the diagnosis and ease the workload of pathologists.

关键词： deep learning convolutional neural network cancer diagnosis machine learning histopathology image

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Anti-forensic Analysis for image Splicing Detection Through Advanced Filters 1

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2nd International conference on Advances in data-driven Computing and Intelligent Systems, ADCIS 2023

作者： Kumar, Nitish Meenpal, Toshanlal Ahmad, Muhammed Yaseen Department of Electronics and Communication Engineering National Institute of Technology Raipur Raipur India

ISBN: (数字)9789819995240

ISBN: (纸本)9789819995233

Nowadays, manipulated images can be found everywhere due to the wide availability of sophisticated image editing tools. As the digital technology advances for multimedia, the field of image forensic has also made significant progress in recent years with the development of effective and more advanced image tampering detection methods. Furthermore, there has been continuous development in the image tampering methods along with the techniques used for hiding tampering clues aiming to evade the existing forensic approaches. image splicing is one of the most common and simple image tampering approach, where an object or region is copied from one image and stitched into the other image. This paper presents an anti-forensic analysis on three advanced filters: the weighted average filter, bilateral blur filter, and Kuwahara filter. The objective of this analysis is to examine the impact of filtering operations on the detection accuracy of image splicing. Two deep learning-based image splicing detection models are proposed which are initialized with pre-trained weights of ResNet and InceptionNet architecture. To assess the effect of filtering, the structural similarity index measure (SSIM) is employed to quantify the similarity between images before and after the application of the filters. Based on the experimental results, the weighted average filter and Kuwahara filter emerge as the most effective anti-forensic attack on spliced images. These filters demonstrate the ability to preserve the SSIM of the image while inducing a substantial decrease in the accuracy of splicing detection. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep learning

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Research on image Processing Algorithms Based on Variational and Deep Learning

Research on Image Processing Algorithms Based on Variational...

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Mechatronics, IoT and Industrial Informatics (ICMIII), International conference on

作者： Xiaoxu Zhang Heilongjiang Polytechnic Harbin China

ISBN: (数字)9798350386639

ISBN: (纸本)9798350386646

China has seen an unheard-of surge in interest in deep-learning methods for image restoration in recent years. Most of these strategies draw inspiration from the established variational technique and related optimization methods for the picture reconstruction inverse issue. While using learnable components to create organized deep neural networks and using copious amounts of observation data to train the networks for the particular reconstruction objectives, these techniques resemble the iterative strategies of ordinary optimization algorithms. In many cases, they have proven to have far better empirical performance than the conventional approaches, and they also demand a lot lower computing cost. For various common networks in this subject, this research offers the specifics of the derivations, the network topologies, and the training protocol. The research therefore focuses on image processing algorithms based on variational and deep learning models.

关键词： Training Deep learning Sensitivity Protocols Optimization methods Visual systems Retina

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Enhanced Detection of Oral Squamous Cell Carcinoma from Histopathological images using Convolutional Neural Network Architectures 2

Enhanced Detection of Oral Squamous Cell Carcinoma from Hist...

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2nd International conference on Intelligent Cyber Physical Systems and Internet of Things (ICoICI)

作者： Kaur, Gurjot Sharma, Neha Gupta, Rupesh Chitkara Univ Inst Engn & Technol Rajpura Punjab India

ISBN: (纸本)9798331540661;9798331540678

A major worldwide health issue marked by high morbidity and death;oral squamous cell carcinoma is caused mostly by delayed detection. Early identification of oral cancer using immediate medical intervention improves patient survival rates and therapy results. In this work, we built an image classification system to detect oral squamous cell carcinoma (OSCC) in histopathology images using a customized convolutional neural network (CNN). Normal and OSCC classes comprised the dataset's 4,946 training images, 126 test images, and 120 validation images. The model's resilience was improved using rescaling, shearing, zooming, and horizontal flipping among data augmentation methods. Comprising many convolutional, pooling, and dropout layers, the CNN model was trained using the Adam optimizer and category cross-entropy loss function. Following thirty epochs, the model attained 86.67% validation accuracy and 94.64% training accuracy. The model's performance on the test set was further evaluated generating an overall accuracy of 72%, a recall of 92%, and a precision of 76%. These results highlight the potential of the CNN-based approach in supporting pathologists in early oral cancer detection, hence improving clinical judgment and patient treatment. The outstanding accuracy highlights how well deep learning models analyze medical images and their possible use in clinical operations to raise diagnosis accuracy and results. The CNN-based image classification system heralds a revolution in early oral cancer detection by providing a consistent and fast tool for doctors fighting this common illness.

关键词： Oral squamous cell carcinoma (OSCC) Histopathology images Convolutional Neural Network (CNN) Medical image Classification Early Detection

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Sign Language to Text Classification using One-Shot Learning 2

Sign Language to Text Classification using One-Shot Learning

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2nd International conference on Sustainable Computing and data Communication Systems, ICSCDS 2023

作者： Pal, Sourav Ruhella, Sourabh Sinha, Sparsh Goel, Anurag Delhi Technological University Dept. of Computer Science and Engineering New Delhi India

ISBN: (纸本)9781665491990

Sign language is utilized by deaf individuals to communicate with each other. Sign languages, simply put are signs and gestures made by hand and are currently being used by approximately 80 million people around the world. The limited knowledge of sign languages in the hearing-abled community makes it difficult for them to interact with the deaf or mute community leading to the marginalization of the latter. Sign-to-text classification is a field of research that aims to convert sign language into written text, providing a communication bridge between the deaf-mute communities and the hearing population. However, there are some limitations in the current state of the field, such as the absence of datasets that are publicly available for sign language in many languages, and most of the existing models are trained in American Sign Language (ASL) which makes it difficult for researchers to train and evaluate their methods on other sign languages. Additionally, the ASL-trained models may generalize poorly to other sign languages, as they are heavily tuned to the specific dataset of ASL signs. The current models are also not best suited to incorporate any new additions to the sign language as it requires to train the model all over again. The proposed model makes use of one-shot learning, a new-age machine learning technique that allows the model to learn and recognize using limited training data, to identify and classify hand signs and convert them into corresponding text. The ability to learn from a very small dataset makes the model easily trainable and expandable in any sign language of choice. The proposed model has been trained on ASL and Indian Sign Language (ISL) datasets and achieved accuracy of 90.03% and 91.25% respectively. © 2023 IEEE.

关键词： image classification

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Automated Injury Detection and Alert Systems in Public Transportation Integrating IoT with Convolutional Neural Networks 2

Automated Injury Detection and Alert Systems in Public Trans...

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2nd International conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Vimal, S.P. Peter, John Benito Jesudasan Kavitha, U. Bhuvaneswari, G. Manikandan, G. Thamizhamuthu, R. Sri Ramakrishna Engineering College Department of Electronics and Communication Engineering Tamil Nadu Coimbatore India Capital One Services Llc RichmondVA United States Vel Tech RangarajanDr.Sagunthala R&d Institute of Science and Technology School of Commerce Tamil Nadu Chennai India Saveetha Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Rmk Engineering College Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur Campus-Chengalpattu Tamil Nadu Chennai India

ISBN: (纸本)9798350368413

Innovative methods to improve public transportation safety and response mechanisms have emerged due to Internet of Things (IoT) and artificial intelligence (AI) technologies. IoT devices and Convolutional Neural Networks (CNNs), a class of deep learning algorithms analyzing visual imagery, are used to create an innovative Automated Injury Detection and Alert System (AIDAS) for public transportation. The proposed AIDAS architecture employs IoT sensors and cameras in buses and trains to monitor and evaluate passengers' physical health in real time. These sensors can detect unexpected impacts, odd motions, and distressing noises. A strong CNN model trained to detect passenger injury or bodily damage receives live images from the onboard cameras. An automatic alarm process is activated immediately upon injury detection. The closest emergency response teams and transportation system operators get complete event reports, including time, position (by GPS coordinates), and injury type. A quick reaction to accidents with this real-time alarm system might save lives and reduce public transportation injuries. AIDAS' use of IoT and CNN technology is innovative in public transportation safety. The technology significantly improved emergency response times and reduced unattended injuries in public transport networks. The system's excellent damage detection accuracy is due to CNNs' advanced image processing and pattern recognition skills and IoT devices' sensory inputs. It also addresses privacy issues and the necessity for strong data encryption and proposes solutions. Finally, it analyzes how AIDAS affects public transportation safety and future research and development in this critical field. © 2024 IEEE.

关键词： Convolutional neural networks

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Rapid Retrieval System of UAV image Based on Deep Learning 2

Rapid Retrieval System of UAV Image Based on Deep Learning

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2nd International conference on Geographic Information and Remote Sensing Technology, GIRST 2023

作者： Xue, Yating Wu, Shengwei Wang, Jiangtao Chen, Jieping China Coal Aerophotogrammetry & Remote Sensing Group Co. Ltd. Xi’an710199 China China Coal Geology Xi’an Map Printing Co. Ltd. Xi’an710199 China Chengdu University of Information Technology Cheng du610225 China

ISBN: (纸本)9781510668478

The rapid development of unmanned aerial vehicle (UAV) aerial survey technology and deep learning has revolutionized traditional aerial survey methods. With its fast measurement, low cost, good safety, and high reliability, UAVs can quickly obtain image data of a certain survey area in a relatively short time. Although UAV surveying has been used in various fields of related industries, there are still certain limitations to quickly obtaining desired information from a group of images. Therefore, this paper combines the VGG16 deep learning model to match UAV images and make it applicable to the web environment. Starting with extracting image features of UAVs from the VGG16 model, this paper implements a UAV image feature extraction and retrieval system based on VGG16. Then, the matching efficiency is compared with the matching model based on the SIFT algorithm. Finally, the image feature extraction and retrieval system based on VGG16 is combined with the web system to study the retrieval of image data of interested areas or image data without spatial information, as well as spatial information queries under web conditions. © 2023 SPIE.

关键词： Unmanned aerial vehicles (UAV)

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Detection & Diagnosis of COVID-19 from CXR images Through VGG19 Transfer Learning Model 2

Detection & Diagnosis of COVID-19 from CXR Images Through VG...

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2nd IEEE International conference on Futuristic Technologies, INCOFT 2023

作者： Pillai, Rudresh Sharma, Neha Chauhan, Rahul Verma, Garima Gupta, Rupesh Chitkara University Institute of Engineering & Technology Chitkara University Punjab Rajpura India Computer Science and Engineering Graphic Era Hill University Uttarakhand Dehradun India Computer Science & Engineering Graphic Era Deemed to Be University Uttarakhand Dehradun248002 India

ISBN: (纸本)9798350308846

COVID-19 must be diagnosed rapidly and precisely to control the current pandemic effectively. Using a dataset of 21,165 chest X-ray images, this study suggests a transfer learning approach for identifying and stratification of COVID-19. Three subsets of the dataset - training, validation, and test - each including 16,932, 2,116, and 2,117 images - are created. The VGG19 transfer learning model uses deep learning approaches to extract pertinent image characteristics and accurately classify the input images. By reaching a high accuracy rate, the key objective of this study is to distinguish COVID-19-infected patients from others with distinct lungrelated abnormalities. The model successfully classifies images with an incredible 94.85% accuracy, highlighting its potential for biomedical applications in the real world. The model's robustness and generalizability are aided by the extensive and varied dataset, which includes four annotated classes. The VGG19 model has much to offer regarding healthcare advantages if successfully implemented. The speed and accuracy of diagnosis can be improved by automatically identifying and classifying COVID-19 instances from chest Xray images, allowing for quicker patient triage and resource allocation. Additionally, it can lighten the load on radiologists and health workers and promote efficient management tactics throughout the pandemic. This study emphasizes the potential of models based on deep learning for COVID-19 identification in digital image processing. Future studies should improve the model's effectiveness, determine if it can be used in various groups, and include more clinical data for thorough research. In the continuing COVID-19 pandemic, the proposed model may help medical practitioners identify patients more quickly and accurately, leading to better patient outcomes. © 2023 IEEE.

关键词： COVID-19 Deep Learning Digital image Processing International Health Regulations Preventable Deaths VGG19

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Comparative Study on Intelligent Modulation Recognition Models Based on Raw data 2

Comparative Study on Intelligent Modulation Recognition Mode...

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2nd International conference on image Processing, Computer Vision and Machine Learning, ICICML 2023

作者： Xie, Didi Zhao, Yurui Wang, Haoyuan Huang, Zhitao Wang, Xiang National University of Defense Technology College of Electronic Science and Engineering Changsha China People's Liberation 61905 Army of China Guangzhou China

ISBN: (纸本)9798350331417

In recent years, intelligent modulation recognition methods for communication signals based on deep learning have attracted attention due to their advantages in feature extraction and recognition performance. However, there is limited research on the performance boundaries and optimization criteria of neural network models in existing domestic and international studies, and the datasets used in the research are relatively simple. This paper first proposes an intelligent modulation recognition model based on deep learning;Then a super dataset containing 22 traditional and emerging complex modulation modes is generated by simulation;Finally, based on this dataset, a comprehensive comparative analysis is conducted from multiple perspectives to investigate the effects of data slicing, selection and design of network models and parameters on the modulation recognition performance of the model. General patterns were obtained, providing valuable references for subsequent research and model application. © 2023 IEEE.

关键词： data Preprocessing Deep learning Intelligent Modulation Recognition Network Model Parameter Optimization

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2nd International conference on Unmanned Aerial System in Geomatics, UASG 2021

2nd International Conference on Unmanned Aerial System in Ge...

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2nd International conference on Unmanned Aerial System in Geomatics, UASG 2021

ISBN: (纸本)9783031193088

The proceedings contain 39 papers. The special focus in this conference is on Unmanned Aerial System in Geomatics. The topics include: Drone Technology in Waste Management: A Review;Solar Roof Panel Extraction from UAV Photogrammetric Point Cloud;Spacio-Statistical Model to Predict Crime Locations Based on Past Crime Events and UAV Based Monitoring of the Predicted Surveillance Route;Automatic Ship Detection Using CFAR Algorithm for Quad-Pol UAV-SAR imagery;A Deep Learning Approach for Detection and Segmentation of Airplanes in Ultrahigh-Spatial-Resolution UAV dataset;Influence of European UAS Regulations on image Acquisition for 3D Building Modeling;Effects of Flight Plan Parameters on the Quality and Usability of Low-Cost UAS Photogrammetry data Products for Tree Crown Delineation;The Segmentation of Drone image derived 3D Point Cloud Using a Combination of RANSAC, DBSCAN and Clustering Methods;UAV to Cadastral Parcel Boundary Translation and Synthetic UAV image Generation Using Conditional-Generative Adversarial Network;An Automated Process to Filter UAS-Based Point Clouds;some Enhancement of Aerial and Terrestrial Photo for 3D Modeling of Texture-Less Object Surface;role of Drone Technology in Sustainable Rural Development: Opportunities and Challenges;disaster Risk Mapping from Aerial imagery Using Deep Learning Techniques;High-Resolution Mapping of Forest Canopy Cover Using UAV and Sentinel-2;design and Method of an Agricultural Drone System Using Biomass Vegetation Indices and Multispectral images;UAV-LiDAR and Terrestrial Laser Scanning for Automatic Extraction of Forest Inventory Parameters;A UAS-Based Approach for Orchard Geo-Information Management System;High-Speed Wi-Fi Systems for Long Range FANETS: Real Problems, Experiments, and Lessons Learnt;preface.

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