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Automated Marker-Less Patient-to-Preoperative Medical Image Registration Approach Using RGB-D Images and Facial Landmarks for Potential Use in Computed-Aided Surgical Navigation of the Paranasal Sinus 40th

Automated Marker-Less Patient-to-Preoperative Medical Image ...

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40th computer Graphics international conference (CGI)

作者： Kim, Suhyeon An, Haill Song, Myungji Lee, Sungmin Jung, Hoijoon Kim, Seontae Jung, Younhyun Gachon Univ Sch Comp Seongnam Si South Korea Univ Sydney Sch Comp Sci Sydney NSW Australia Gil Med Ctr Coll Med Dept Otolaryngol Head & Neck Surg Seongnam Si South Korea

ISBN: (纸本)9783031500787;9783031500770

Paranasal sinus surgery is an established treatment option for chronic rhinosinusitis. Because this surgery is performed inside the nasal cavity, where critical anatomical structures, such as optic nerves and pituitary glands, exist nearby, surgeons usually rely on computeraided surgical navigation (CSN) to provide a wide field of view in the surgical site and to allow for precise control of surgical instruments. In the CSNs, it is essential to register the surgical site of the actual patient with the corresponding view from the preoperative computed tomography (CT) images. the traditional registration approaches are performed manually by the user or automatically by attaching fiducial markers on both the patient's surgical site and preoperative CT images for every surgery before use. In this work, we propose an automated approach to register patient-to-preoperative CT image without fiducial markers. the proposed approach detected and extracted facial anatomical landmarks in 2D RGB images through the use of deep learning models. these landmarks were located in 3D facial mesh reconstructed from depth images by using unprojection and ray-marching algorithms. the facial landmark pairs acquired from the patient site and the preoperative CT images are then registered with singular value decomposition and iterative closet point algorithms. We demonstrate the registration capability of our approach using Microsoft HoloLens 2, a mixed reality head-mounted display because it facilitates the acquisition of RGB-depth images and the prototype development of in-situ visualization to illustrate how the CT images are properly registered on the target surgical site. We compared our automated marker-less registration approach to the manual counter-part using a facial phantom with three participants. the results show that our approach produces relatively good registration accuracy, with a marginal target registration error of 4.4mm when compared to the manual counterpart.

关键词： Automated marker-less registration computer-aided surgical navigation Paranasal sinus surgery Facial landmarks

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Advancements in Brain Tumor Detection using Machine Learning Applications from MRI Image Analysis

Advancements in Brain Tumor Detection using Machine Learning...

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international conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

作者： Yerram Sneha Y Mohana Roopa Padmini Sawant M Varaprasad Rao D Lakshmi Padmaja R.V.S. Lalitha Department of CSE KL University Hyderabad India Department of CSE Institute of Aeronautical Engineering Hyderabad Telangana India Department of Mechanical Engineering Bharati Vidyapeeth College of Engineering Navi Mumbai India Department of Computer Science and Engineering CVR College of Engineering Hyderabad India Department of Information Technology Anurag University Hyderabad Telangana India Department of Computer Science and Engineering Aditya College of Engineering & Technology India

the present research uses a dataset of 2034 images to conduct a detailed evaluation of machine learning models for the purpose of identifying brain tumours in MRI scans. Convolutional Neural Networks (CNN), Support Vector Machines (SVM), Random Forest (RF), and Logistic Regression (LR) were four alternative models that were thoroughly examined based on their performance indicators. the Convolutional Neural Network was shown to be the most effective model, with a high accuracy of 97.5%, great precision, recall, and F1 scores. this demonstrates how deep learning, namely CNNs, can be used to automate and improve brain tumour identification in medical imaging. the study also underlines the SVM and RF models' durability and adaptability, which demonstrated exceptional performance metrics and were thus acceptable for use in real-world healthcare applications. Despite having substantially lower scores, the Logistic Regression model also significantly aids in diagnosis. Finally, this study demonstrates the importance of machine learning in the medical business and the potential for early brain tumour detection, which would improve patient care and treatment outcomes. It emphasises how the field of medical image analysis is always evolving and improving, meaning advances in the detection and management of major medical disorders.

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Geofencing and Efficient Crop Prediction using Government Documents 7

Geofencing and Efficient Crop Prediction using Government Do...

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7th IEEE international conference on advances in Computing, Communication and Control, ICAC3 2021

作者： Karthikraj, V. Patil, Varsha thanneermalai, Senthil Murugesh, Venkatesh Sies Graduate School of Technology Department of Computer Science India

ISBN: (纸本)9781665426343

the government of Maharashtra relies on the SaatBaara (7 / 12) document to identify the ownership of the land [4]. In addition, it uses this document to classify any particular land as agricultural or commercial. However, in many cases there is a constant dispute among farmers regarding the boundaries of their lands. the documents only contain the landmarks and do not contain the exact coordinates of the land. therefore, it is difficult to exactly demarcate the boundaries in real life. this system extracts the landmarks from the documents and pinpoints them on the map. these pinpointed landmarks can be easily readjusted by the farmer to determine the exact location of the land. Further, the farmer determines the crop that they need to sow, based on the past experience and current trend in the market prices and other factors. However, in many of the cases this proves to be inaccurate. therefore, this system will assist the farmer on determining the type of crops that needs to be sowed on their land for maximum profit based on the documents like Soil Health Card and 7/12 document. As a result, this system can be considered the farmer's best friend ranging from the demarcation of the boundaries to crop prediction. © 2021 IEEE.

关键词： Crops

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Blockchain Based Security Framework for Internet of Medical things (IoMT) Applications 23

Blockchain Based Security Framework for Internet of Medical ...

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Proceedings of the 2023 7th international conference on advances in Artificial Intelligence

作者： Pashupati Baniya Parma Nand Bharat Bhushan Alaa Ali Hameed Akhtar Jamil Computer ScieDepartment of Computer Science and Engineering School of Engineering & Technologynce and Enginerring Sharda University India Department of Computer Engineering Istinye University Turkey Department of Computer Science National University of Computer and Engineering Sciences Pakistan

ISBN: (纸本)9798400708985

the growing demand for Internet of Medical things (IoMT) sensors within the healthcare sector has emphasized the necessity for robust security measures. However, these devices, often constrained by limited resources, frequently require external assistance to guarantee the security of data. Blockchain technology emerges as a practical solution, offering a way to enhance system security and safeguard patients' sensitive health records against unauthorized access and data breaches. the fusion of IoMT and blockchain presents a range of benefits, such as cost reduction, improved speed, automation, and the guarantee of unmodifiable data. Nevertheless, security concerns persist, particularly in light of the ever-evolving encryption techniques. the effective implementation of IoMT hinges on the delivery of personalized, patient-centered care, which has the potential to enhance affordability, simplicity, and overall convenience, ultimately contributing to improved quality of life and extended lifespans. In this comprehensive exploration, we delve into the historical context, architecture, components, and challenges associated with IoMT. Additionally, we conduct an in-depth examination of blockchain technology, covering its essential elements and categories, as well as its seamless integration into IoMT, underscoring its critical role in reinforcing security measures within the healthcare domain. Furthermore, this paper outlines recent developments in blockchain technology within the IoMT framework, highlighting its ongoing evolution and sustained significance in the healthcare field.

关键词： Blockchain Healthcare Industry IoMT Privacy Security

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Disease Identification in Tomato Leaf Using Pre-trained ResNet and Deformable Inception 1

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5th IFIP TC 12 international conference on Computational Intelligence in Data science, ICCIDS 2022

作者： Ahuja, Arnav Tulsyan, Aditya Ranjani, J. Jennifer Department of Computer Science and Information Systems and Department of Mathematics Birla Institute of Technology and Science Pilani333031 India Virufy San Francisco United States

ISBN: (数字)9783031163647

ISBN: (纸本)9783031163630

Disease in crops is a growing concern for the food and farming industry. In the last couple of decades, lack of immunity in plants and extreme climate conditions due to drastic climate changes have caused a significant increase in the growth of diseases among crops. On a large scale, these diseases cause eventual financial loss to the farmers due to a decrease in farming. Fast and early detection of the disease remains a challenge in most parts of the world because of the lack of robust research infrastructure. Automated techniques for disease detection are high in demand with a worldwide increase in image capturing and video recording devices, together with the continuous evolution in computer vision and machine learning. In this paper, a neural network model, trained on publicly available data which comprise both healthy and leaves with the disease, is proposed. the model adopts a new approach combining the ResNet and the InceptionNet architectures. Skip connections and the 1 × 1 convolutions in both the architectures are put to good use here. We achieve an accuracy of 99.08% in the PlantVillage dataset [17] and reasonable accuracy of 66.06% on the PlantDoc dataset [16] which is an increment of more than 25% from the approaches in the previous works. this paper also suggests a method to improve the detection of diseases in crops in the real world by augmenting the number of data points. We have discussed the use of deformable convolution, which is capable of learning various geometric transformations and can improve the performance of the architecture. © 2022, IFIP international Federation for Information Processing.

关键词： Crops

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Overview of Second Shared Task on Homophobia and Transphobia Detection in Social Media Comments 3

Overview of Second Shared Task on Homophobia and Transphobia...

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3rd Workshop on Language technology for Equality, Diversity and Inclusion, LTEDI 2023

作者： Chakravarthi, Bharathi Raja Ponnusamy, Rahul Subramanian, Malliga Buitelaar, Paul García-Cumbreras, Miguel Ángel Jiménez-Zafra, Salud María García-Díaz, José Antonio Valencia-García, Rafael Jindal, Nitesh School of Computer Science University of Galway Ireland Insight SFI Research Centre for Data Analytics University of Galway Ireland Kongu Engineering College Tamil Nadu India Universidad de Jaén Spain Universidad de Murcia Spain University of Galway Ireland

ISBN: (纸本)9789544520847

We present an overview of the second shared task on homophobia/transphobia Detection in social media comments. Given a comment, a system must predict whether or not it contains any form of homophobia/transphobia. the shared task included five languages: English, Spanish, Tamil, Hindi, and Malayalam. the data was given for two tasks. Task A was given three labels, and Task B fine-grained seven labels. In total, 75 teams enrolled for the shared task in Codalab. For Task A, 10 teams submitted systems for English, 7 for Tamil, 4 for Spanish, 7 for Malayalam, and seven teams for Hindi. For Task B, 8 teams were submitted for English, 7 for Tamil, and 6 for Malayalam. We present and analyze all submissions in this paper. © 2023 LTEDI 2023 - 3rd Workshop on Language technology for Equality, Diversity and Inclusion, associated with the 14th international conference on Recent advances in Natural Language Processing, RANLP 2023 - Proceedings. All rights reserved.

关键词： Social networking (online)

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Performance Comparison of Convolutional Neural Network Deep Learning Architectures for Remote Sensing Image Segmentation 4th

Performance Comparison of Convolutional Neural Network Deep ...

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4th international conference on advances in Computational science and Engineering, ICACSE 2023

作者： Shoaib, Asim Vadiveloo, Mogana Lim, Seng Poh Department of Computer Science Faculty of Information and Communication Technology Universiti Tunku Abdul Rahman Jalan Universiti Bandar Barat Perak Kampar31900 Malaysia

ISBN: (纸本)9789819729760

In this paper, the performance of five convolutional neural network (CNN) deep learning architectures were evaluated for delineating high complexity building regions in remote sensing (RS) images. they are the U-Net, SegNet, ResU-Net, AttentionU-Net, and V-Net architectures that are commonly used for image segmentation. In the existing works, the robustness of these deep learning architectures was evaluated in delineating the regions of interest (ROIs) in various RS image as well as segmenting anatomical structures as ROIs in the medical images, individually or in pairs. It is crucial to evaluate the performance efficiency of these architectures on the same image dataset that has high complexity of ROIs for delineation. therefore, the performances of the five mentioned deep learning architectures were evaluated for comparison in segmenting buildings regions as ROIs in the WHU RS images dataset, which consist of various colors, shapes, sizes, and textures features. the experimental results showed that the AttentionU-Net deep learning architecture accurately segmented the buildings regions achieving an average Intersection over Union (IoU) of 0.9780, 0.9436, 0.9469 and average F-measure of 0.9690, 0.9230, 0.9301 for training, validation, and test images, respectively with 62 epochs in comparison to the other four architectures. this is mainly because of the incorporation of the attention gates (AGs) in the AttentionU-Net architecture that are able to extract the fine feature details of the complex building regions in the WHU RS images dataset. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

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Efficient Iterative Semi-supervised Object Detection Training for Counting of Fruits and Vegetables 23

Efficient Iterative Semi-supervised Object Detection Trainin...

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Proceedings of the 2023 7th international conference on advances in Artificial Intelligence

作者： Kirill Bogomasov Stefan Conrad Department of Computer Science Heinrich Heine University Germany

ISBN: (纸本)9798400708985

Object recognition could be successfully applied to problems such as object counting [3]. this transition makes this kind of technology interesting for numerous industries where automation and quality assurance based on visual information are possible. A major limitation to the practical usage of object recognition models is that each time the data is changed e.g. new classes are added, the model must be retrained which usually requires manual annotation of the new data. this entails not only a continuous annotation effort, which leads to higher costs but also an increased deployment period for updated models. In this paper, we address the issue of reducing the manual effort for annotation based on the example of fruits and vegetables recognition and counting in retail applications. Furthermore, we present an iterative semi-supervised training strategy for handling this task. We achieve a reduction of the manual effort of more than 60% while maintaining the counting accuracy of 83% on 36 different object classes.

关键词： neural networks object counting object detection semi-supervised learning

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Structural Design of Convolutional Neural Network-Based Steganalysis 7th

Structural Design of Convolutional Neural Network-Based Steg...

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7th international conference on Advanced Computing, Networking, and Informatics, ICACNI 2019

作者： Mandal, Pratap Chandra Department of Computer Science and Engineering B.P. Poddar Institute of Management and Technology Kolkata700052 India Department of Computer Science and Engineering Indian Institute of Information Technology Kalyani Kalyani India

ISBN: (纸本)9789811586095

A tailored Convolutional Neural Network (CNN) model aimed at steganalysis has been designed. the model is able to acquire the complicated dependencies which are necessary for steganalysis. the usefulness of the model has been demonstrated on the state-of-the-art method S-UNIWARD at 0.4 bpp. the model achieves F1 score equal to 0.849 and detection error 0.152 which are comparable performance. © 2021, the Editor(s) (if applicable) and the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Neural network models

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A Study on Integrating Machine Learning Techniques for Waste Management

A Study on Integrating Machine Learning Techniques for Waste...

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international conference on Circuit, Power and Computing Technologies (ICCPCT)

作者： Ruchika Vaidya Rahul Dattangire Divya Biradar Prateek Verma Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (DU) Wardha Maharashtra India Data Engineering Publicis Sapient Houston Texas USA Department of Computer Science University of Texas Arlignton Texas USA

ISBN: (数字)9798350372816

ISBN: (纸本)9798350372823

the current study focuses on how various Machine Learning (ML) algorithms can be used in waste management and emphasizes the significance of intelligent technologies in addressing contemporary issues. By leading a broad writing survey, the paper features the meaning of incorporating these advances into waste management practices. the system segment subtleties the information assortment, preprocessing, model determination, and assessment measurements utilized in the review. the accuracy and performance metrics of various classifiers, including K Nearest Neighbors, Linear Support Vector Classifier, Logistic Regression, Decision Tree, Multi-Layer Perceptron, and Random Forest, are demonstrated by the results of experiments with them. Especially noteworthy is the MLP classifier's 91.81% accuracy. the discoveries highlight the viability of ML approaches in ssmart waste management. In addition, a comparison table that summarizes the classifiers' accuracy scores provides insight into their suitability for applications in waste management.

关键词： Waste management Surveys Productivity Measurement Accuracy Reviews Vectors

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