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Utilizing the U-Net Network for Segmenting the Left Ventricle from Cine-MRI Scans

Utilizing the U-Net Network for Segmenting the Left Ventricl...

引用

Technological Advancements in Computational sciences (ICTACS), International Conference on

作者： Sonal Malhotra Siddhant Thapliyal Priyanshi Aggarwal Mukesh Kumar Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Dept. of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India

ISBN: (数字)9798350387490

ISBN: (纸本)9798350387506

In this research, we studied the U-Net segmentation model for identifying and demonstrating addresses related to cardiac left ventricle contour in cine-MRI images. Efficacy of the model is evaluated across different stages such as preprocessing, training, validation and robustness testing along with comparisons to conventional methods and other deep learning architectures. But the preprocessing includes standardized pixel intensities, and by data augmentation using rotation, flip method or scaling techniques. The training and validation stages clearly show the robust consolidation of our model with little overfitting, guaranteeing decent performance on different datasets. The developed model is robust by passing the test against noise/ artifacts, and providing a very good segmentation in extreme situations so that there are no accuracy outliers represented. Comparative analyses show better sensitivity-specificity trade-offs and precision-recall characteristics versus other methods. In general, a U-Net model developed here works well for cardiac MRI analysis and can be used as an effective tool to help automatic LV segmentation accurate and efficient in clinical applications which will improve diagnosis of heart diseases from imaging points.

关键词： Training Image segmentation Analytical models Accuracy Image recognition Magnetic resonance imaging Deep architecture Robustness Testing Overfitting

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Securing the Future of Electronic Health Records with Blockchain Integration

Securing the Future of Electronic Health Records with Blockc...

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Trends in Electrical, Electronics, computer engineering Conference (TEECCON)

作者： Andleeb Khan Parma Nand Bharat Bhushan Department of Computer Science & Engineering Sharda School of Engineering & Technology Greater Noida UP Dept. of Computer Science & Engineering Sharda School of Engineering &Technology Greater Noida UP

ISBN: (数字)9798331505714

ISBN: (纸本)9798331505721

The use of blockchain technology is meant to revolutionize the way in which traditional systems work. This technology has paved way for more secure solutions thereby leveraging the concepts of data privacy and authentication. Electronic health records are digitally stored health data of the patients which includes their history of diagnosis along with their reports generated by the hospitals. But these records also face the concerns of data privacy and security as they are prone to tempering, leakage and duplication. To address these concerns researchers have come up with the use of blockchain in electronic health records. Since blockchain is a decentralized and immutable ledger-based platform, it offers more secure and private solutions to the healthcare sector. This paper presents a framework based on blockchain for EHRs in order to enhance their security. It also makes use of Interplanetary file system as a distributed data storage platform which is compatible with Web 3 based environments. This addresses the issue of scalability of the system. The proposed framework proves to be better in terms of privacy, scalability, security and cost when compared with the recent literatures. This system can further be extended by integrating multi-factor authentication as a future direction for research.

关键词： Data privacy Privacy Multi-factor authentication Hospitals Scalability Memory Market research Blockchains InterPlanetary File System Electronic medical records

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Administrator Controlled Timetable Based Automatic Facial Recognition Attendance System

Administrator Controlled Timetable Based Automatic Facial Re...

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2023 International Conference in Advances in Power, Signal, and Information Technology, APSIT 2023

作者： Dash, Ankit Jena, Siba Prasad Gantayet, Amaresh Siksha O Anusandhan Deemed to Be University Dept. of Electrical and Electronics Engineering Bhubaneswar India Siksha O Anusandhan Deemed to Be University Dept. of Computer Science & Engineering Bhubaneswar India

ISBN: (纸本)9798350339369

A facial recognition attendance system is a technology-based solution for automating the process of taking attendance in schools, offices, or other organizations. The present work mainly focuses on image capturing, training and recognition in real-time using free source Python modules. The system uses computer vision algorithm such as the Open Source computer Vision (OpenCV) Library to recognize and identify individuals based on their facial features. Additionally, the Numerical Python (NumPy) library is used to store and manipulate arrays of facial recognition data, such as feature vectors or embeddings. The Tkinter GUI is employed as an interface for managing attendance and allows the administrator to access the system and view attendance records for individual students. The ESP32-CAM module and thin-film-transistor liquid-crystal (TFT) display has been used together to provide a live video stream, while the server processes the video and records attendance. This system would improve accuracy of attendance records because it will remove all the hassles of roll calling (in case of classroom attendance of students) and will save valuable time of the teachers as well as students. © 2023 IEEE.

关键词： Open systems

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Integration of Optimization Techniques to Improve Performance of Machine Learning System 8

Integration of Optimization Techniques to Improve Performanc...

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8th International Conference on Advanced Computing and Communication Systems, ICACCS 2022

作者： Shukla, Amogh Lodha, Nitin Vellore Institute of Technology Dept. of Computer Science and Engineering Tamil Nadu Vellore India

ISBN: (纸本)9781665408165

Machine learning (ML), a kind of artificial intelligence, may help software systems become better at anticipating outcomes without explicitly designing them (AI). Machine learning systems use historical data to predict future values. Optimization strategies are a must-have toolkit for effectively managing an organization's resources and maximizing shareholder returns. This study's focus was on finding the best ways for developing machine learning systems. Researchers are using optimization methodologies to improve machine learning performance. Using optimization approaches in research is being explored with the purpose of boosting output. In order to help investors make better judgments, the collection of crypto currencies is currently being used. The optimization will assist in narrowing down the crypto currency dataset. Integrating an optimization method to boost a machine learning system's performance is what the proposed study aims to do. © 2022 IEEE.

关键词： Machine learning

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Validation of an Information Diffusion based Health Social Media on Yoga and Ayurveda 2

Validation of an Information Diffusion based Health Social M...

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2nd International Conference on computer science, engineering and Applications, ICCSEA 2022

作者： Yadav, Rk Kumar, Girish Kumar, Naveen Varshney, Pratyaksh Jha, Natwar Amit Delhi Technological University Dept. of Computer Science & Engg. Delhi India Delhi Technological University Dept. of Mechanical Engineering Delhi India Delhi Technological University Dept. of Computer Science & Engineering Delhi India

ISBN: (数字)9781665458344

ISBN: (纸本)9781665458344

The deadly COVID-19 coupled with other diseases has proven to be the biggest challenge humans have seen in ages. Healthcare systems, even in the most developed countries, have completely shattered during the peak of waves. Thus, leaving millions rendered helpless and alone around the globe. This proves the importance of self-care and immunity to being the best possible way for being healthy. Solutions specific to strengthening immunity are available in the Indian sciences of Yoga and Ayurveda and has been scientifically proven. These have been around in India for ages but there is an immense lack of awareness in India and around the globe regarding that. This review paper aims at filling the lack of awareness by proposing a model and validating it further by collecting and comparing the pre and post-implementation data. © 2022 IEEE.

关键词： COVID-19

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A Machine Learning Framework for Classification of Expert and Non-Experts Radiologists using Eye Gaze Data 7

A Machine Learning Framework for Classification of Expert an...

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7th IEEE International Conference on Recent Advances and Innovations in engineering, ICRAIE 2022

作者： Shamyuktha, R.S. Amudha, J. Aiswariya Milan, K. Dept of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (纸本)9781665489102

Artificial intelligence (AI) techniques enhance the efficiency of radiology data interpretation and reporting process. The paper focuses on classifying a participant as an expert or non-expert based on the features related to saccade, such as saccade latency, saccade amplitude, etc. These features help to understand whether the participant knows the respective field or not. The data used here is how the participant looks at an optic disc image and whether they know where to look precisely. The proposed work used different machine learning models to classify experts and non-expert radiologists and observed that the decision tree gave the highest accuracy of 96%. Finally, we create a dashboard with the help of powerbi, which displays the insights about the expert and non-expert viewing the optic discs. © 2022 IEEE.

关键词： Eye movements

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Performance Evaluation of Deep Learning Architectures for Predicting 3D Dose Distributions in Automatic Radiotherapy Treatment Planning 1

Performance Evaluation of Deep Learning Architectures for Pr...

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1st International Conference on Computational Intelligence and Sustainable engineering Solution, CISES 2022

作者： Jha, Sakshi Sajeev, Neelima Rajesh Marchetti, Aarthi Chandran, Lekshmy P. Abdul Nazeer, K.A National Institute of Technology Dept. of Computer Science and Engineering Kerala Calicut673601 India

ISBN: (数字)9781665480048

ISBN: (纸本)9781665480048

Radiation therapy is a leading treatment methods for cancer today, but its advancement has come at the cost of increased complexities of treatment plans, and increased planning time. The planning technique involves optimizing each plan through repeated trial and error to meet the Organs at Risk (OAR) dose criteria. This paper is a comparative study, analyzing the performance of two deep learning architectures, U Net and Generative Adversarial Network (GAN) in determining the radiotherapy dose distributions received by the tumour and nearby organs. The Dataset is taken from the OpenKBP 2020 AAPM Grand Challenge hosted by CodaLab and consists of 3D head and neck CT scans of patients diagnosed with oropharyngeal cancer. To evaluate the models we use dose scores and DVH scores, evaluation metrics proposed by the OpenKBP challenge. The Dose Volume histogram (DVH) is also used to compare the treatment plans. The dose error for the U Net model was calculated as 3.798 and the DVH error was calculated as 2.732. For the GAN model, we achieved a dose score of 3.850 and a DVH score of 3.215. The U Net model outperformed the GAN in predicting the dose distributions. © 2022 IEEE.

关键词： Generative adversarial networks

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Poster: Empirical Evaluation of SC-MCC Meta Program Efficiency Using Dynamic Symbolic Execution Engine

Poster: Empirical Evaluation of SC-MCC Meta Program Efficien...

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International Conference on Software Testing, Verification, and Validation, ICST

作者： Monika Rani Golla Sangharatna Godboley Dept. of Computer Science and Engineering National Institute of Technology Warangal Warangal India Dept. of Computer Science and Engineering NITMiner Technologies National Institute of Technology Warangal Warangal India

ISBN: (数字)9798331508142

ISBN: (纸本)9798331508159

Exploring all the feasible paths in order to generate test cases is costly when dynamic symbolic execution is considered. Hence, there comes the interpolation concept that minimizes the cost to some extent. The earlier work on custom interpolation introduced a resource annotator that instruments the program and generates multiple meta programs (LLVM IRs) in order to generate optimal MCD/DC-based test cases. The proposed approach leverages a Meta Program Generator (MPG) to create a single meta program that encapsulates SC-MCC sequences within “assert” statements, aligned with their corresponding predicates. The effectiveness of this approach is demonstrated through experiments on benchmark programs, comparing it with traditional methods. The results indicate improved efficiency and the generation of a higher number of feasible SC-MCC sequences, making our approach a promising advancement in software testing and symbolic execution. We experimented with 75 Rigorous Examination of Reactive Systems (RERS) benchmark programs for experimentation. It is observed that our implementation has obtained more feasible SC-MCC sequences in 41 out of 75 programs.

关键词： Software testing Interpolation Costs Automatic programming Accuracy Instruments Benchmark testing Dynamic scheduling Engines

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Utilising Convolutional Neural Networks for the Automated Detection of Diabetic Retinopathy

Utilising Convolutional Neural Networks for the Automated De...

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IEEE International Conference on System, Computation, Automation and Networking (ICSCAN)

作者： R Poornima Lakshmi S. Shopika Yaazhini A Hemalatha M V Saraswathi R. Ponnusamy Dept. of Computer Science and Engineering Chennai Institute of Technology Tamil Nadu India Dept. of Computer Science and Engineering Chennai Institute of Technology Chennai Tamilnadu India

ISBN: (数字)9798331510022

ISBN: (纸本)9798331510039

Diabetic retinopathy has emerged as one of the leading causes of eye diseases among people suffering from long-term diabetes. Indeed, it raises the risk of being blinded without proper detection and treatment. Conventional detection of retinal fundus images by an ophthalmologist is time-consuming and prone to mistakes owing to human intervention. This is particularly a cause for alarm in the wake of the growing incidence of diabetes globally. The need for automated, accurate detection systems for early diagnosis of diabetic retinopathy has never been more relevant. In this work, we develop a deep learning model based on CNN for classifying four stages of diabetic retinopathy, from no DR to proliferative DR. We tested the model in a public dataset to extract features from retinal images and to screen for abnormalities automatically. Our CNN model achieved 98.5% classification and detection accuracy, thus indicating the ability to make a real difference in the early detection and treatment plan, thereby preserving the vision of many diabetic patients. Practical value The present research is very relevant to clinical practice and, therefore, practically useful because of its relevance to healthcare technology. Using accuracy and loss function metrics, the proposed model performs well compared to the latest techniques in DR detection. An approach based on CNNs is expected to ease much of the workload that healthcare professionals bear in diagnosis and improve the precision, potentially resulting in a very high cutback in vision loss in diabetic patients with better patient outcomes. Our study has highlighted the practical utility of the CNN model, which has improved patient outcomes. DR is a serious condition affecting the eyes, which, in case of untimely detection and untreated in diabetic patients, can cause loss of vision. For centuries, the conventional diagnosis for this disease has been through the manual inspection of retinal fundus images taken through a camera

关键词： Deep learning Diabetic retinopathy Technological innovation Accuracy Automation Prevention and mitigation Retina Feature extraction Loss measurement Convolutional neural networks

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Investigating the Role of Artificial Intelligence in Building Smart Contact on Block-Chain 4

Investigating the Role of Artificial Intelligence in Buildin...

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4th IEEE International Conference on Emerging Smart Computing and Informatics, ESCI 2022

作者： Shukla, Amogh Lodha, Nitin Vellore Institute of Technology Dept. of Computer Science and Engineering Tamil Nadu Vellore India

ISBN: (纸本)9781665400732

Demand of block-chain is increasing day by day. On other hand smart contract are frequently used in commercial application. Artificial intelligence mechanism is capability to making things intelligent. Proposed paper is investigation role of artificial intelligence in development of smart contract that are block chain based. Deep learning approach is the feature of Artificial intelligence that could make the smart contract running on block chain more efficient and smart. Several existing researches that are made in area of block chain and AI have been considered in present research. The issues faced in previous research are their limited scope and lack of accuracy and performance. The proposed work is supposed to provide better solution in term of accuracy and performance. © 2022 IEEE.

关键词： Smart contract

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