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Enhancing Liver Cancer Detection with AI-Powered image processing

Enhancing Liver Cancer Detection with AI-Powered Image Proce...

引用

Sustainable Expert Systems (ICSES), International conference on

作者： A. Suresh Kumar S. Rajathi Vineetha Vargheese M. Sudha Anju A P. Lokeshkiran Department of Computer Science and Engineering Rathinam Technical Campus Coimbatore Tamil Nadu India Department of Information Technology Karpagam Institute of Technology Coimbatore Tamil Nadu India Department of Electronics and Communication Engineering Rathinam Technical Campus Coimbatore Tamil Nadu India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

this project aims to identify liver tumors using deep learning techniques. the core of our approach is a sophisticated neural network trained on real CT scans of patients with liver tumors. By leveraging advanced signal-processing algorithms and deep learning models, our AI method accurately distinguishes between tumors and healthy liver tissue. Accurate identification of liver tumors on CT images is crucial for planning effective radiation therapy. CT and MRI scans provide detailed images of the internal structure of the body by analyzing tissues. Our primary objective is to develop an AI-driven neural network that can reliably identify liver tumors in these images. Given the variability in liver location among individuals, our approach employs specific techniques to ensure precision. Using U-net technology, our goal is to enhance the speed and accuracy of tumor identification, thereby improving patient access to timely and accurate diagnoses. this project underscores the significant potential of deep learning applications in health care.

关键词： Deep learning image segmentation Accuracy Computed tomography Neural networks Liver Planning Radiation therapy Object recognition Tumors

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A Modified Bilateral Filter for SAR image Speckle Reduction

A Modified Bilateral Filter for SAR Image Speckle Reduction

引用

2012 4th International conference on signal processing Systems(ICSPS 2012)

作者： Jincai Li Yuxing Peng Weimin Zhang Sixun Huang School of Computer Science National University of Defense Technology Institute of Meteorology PLA University of Science and Technology

Bilateral filter can not only smooth images, but also preserve edges. the filtering result is always influenced by the variance of spatial closeness and that of gray value similarity. It is difficult to configure two parameters to the optimum. In this paper, the parameter estimation method proposed in [11] was improved, and a modified bilateral filter was presented. De-speckling experiments on real SAR image and their quantitative measure demonstrate new method is superior to that in [11] on smoothing images and preserving edges.

关键词： synthetic aperture radar, bilateral filter, speckles reduction

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A new robust observer-based sliding-mode control design for uncertain discrete systems with time-varying delay and subject to Hölder nonlinearities

A new robust observer-based sliding-mode control design for ...

引用

conference on Control and Fault-Tolerant Systems (SysTol)

作者： Sonia Ghrab Sofiane Ahmed-Ali Nicolas Langlois Hassani Messaoud Laboratory of Control Signal and Image Processing University of Monastir Monastir Tunisia Normandie Univ Laboratoire IRSEEM Saint-Etienne-du-Rouvray France

ISBN: (纸本)9781728103815

In this paper, we design a new robust Observer-based Discrete-Time Sliding Mode Control for an uncertain linear discrete-time delay system. the considered system is subject to time-varying states delay, unmatched Unknown and norm-Bounded UBB parametric uncertainties, external disturbances and nonlinearities which are assumed to satisfy a Hölder condition. A new delay-dependent sufficient conditions guaranteeing the robust asymptotic stability of the closed-loop system is derived in terms of Linear Matrix Inequalities. With respect to these conditions, a new discrete-time sliding mode control law is designed based on a novel observer structure and a new sliding surface. A numerical example is presented to illustrate the efficiency of our proposed approach.

关键词： Observers Time-varying systems Delays Sliding mode control Symmetric matrices Uncertainty Asymptotic stability

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PARTICLE SWARM OPTIMIZATION（PSO）OF POWER ALLOCATION IN COGNITIVE RADIO SYSTEMS WIth INTERFERENCE CONSTRAINTS

PARTICLE SWARM OPTIMIZATION（PSO）OF POWER ALLOCATION IN COG...

引用

2011 4th IEEE International conference on Broadband Network and Multimedia Technology

作者： Saeed Motiian Mohammad Aghababaie Hamid Soltanian-Zadeh Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering Colleague of EngineeringUniversity of TehranTehran 14399-57131Iran School of Cognitive Sciences Institute for Research in Fundamental Sciences(IPM)Tehran 19548-56316 Iran Image Analysis Laboratory Radiology DepartmentHenry Ford HospitalDetroitMichigan 48202USA

Cognitive radio is used for enhancement of spectrum *** many works have been accomplished on the power allocation of cognitive radio,limited efforts have considered evolutionary *** this paper,we study this problem in the cognitive radio networks where interference constraints are defined for protection of quality of service（QoS）for both primary and secondary *** defined as functions of the signal-to-interference-plus-noise ratio（SINR） are matched for each secondary user which meets Nash’s *** general,the region of utilities that meets the constraints is *** is possible to make simplifications,generate a convex region,and then use common convex optimization approaches to obtain a ***,Particle Swarm Optimization（PSO）does not need such simplifications and thus its results are superior to those of the convex optimization *** is an evolutionary algorithm based on social intelligence,utilized in many optimization *** is a global optimizations algorithm that does not require the objective function be differentiable as required in classic optimization methods.

关键词： Cognitive radio Power allocation Particle Swarm Optimization（PSO）.

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Non-Contact Breathing signal and Rate Estimation Based on EEMD Using IR-UWB

Non-Contact Breathing Signal and Rate Estimation Based on EE...

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International conference on Smart Sensors and Application (ICSSA)

作者： Siti Mahfuzah Fauzi Latifah Munirah Kamarudin Ammar Zakaria Muhammad Husaini Nadri Faculty of Electronic Engineering & Technology Universiti Malaysia Perlis Perlis Malaysia Faculty of Information & Communication Technology Universiti Tunku Abdul Rahman Perak Malaysia

ISBN: (数字)9798350354294

ISBN: (纸本)9798350354300

Impulse Radio Ultra-Wideband (IR-UWB) radar technology has great promise for non-contact sensing applications, especially for detecting human vital signs like breathing rate (BR) and figuring out a person's range. A methodology for determining the breathing frequency and range of stationary sit-still individuals at four distinct ranges-1 $m$ , 1.5 m, 2 m, and 2.5 m-is shown in this work. the preprocessing methods suggested by the study include average subtraction and the Discrete wavelet Transform (DWT). In order to determine the human BR in the frequency domain, the chosen bin is decomposed using Ensemble Empirical Mode Decomposition (EEMD) and processed using the Fast Fourier Transform (FFT). An X4M03 (IR-UWB) radar was used to collect data for the assessment of the approach, and Python software was used to handle the raw signals that were reflected. According to the experiment results, the subjects' accuracy was maximum at a range of 1 $m$ , scoring 93.07%.

关键词： Accuracy Fast Fourier transforms Radar measurements Radar detection signal processing Software Discrete wavelet transforms History Ultra wideband technology Python

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Detection of Spinal Fractures Using image Enhancement Method and Comparison of PSNR and MSE Matric Based on CT-Scan images

Detection of Spinal Fractures Using Image Enhancement Method...

引用

Electronic and Electrical Engineering and Intelligent System (ICE3IS), International conference on

作者： Sri Rahmawati Iskandar Fitri Gunadi Widi Nurcahyo Information System Department Universitas Putra Indonesia YPTK Padang Indonesia Information Technology Department Universitas Putra Indonesia YPTK Padang Indonesia

ISBN: (数字)9798350378368

ISBN: (纸本)9798350378375

this study explores the enhancement of diagnostic capabilities in Computed Tomography (CT) scans for detecting spinal fractures through advanced image processing techniques. the vertebral column's crucial role in physiological functions across all age groups necessitates precise imaging to identify impairments causing severe pain. the research focuses on preprocessing methods, particularly the cropping of the lumbar region to reduce intrinsic noise and improve CT image clarity. Advanced enhancement techniques such as sharpening and contrast stretching are applied to augment image quality, facilitating the precise identification of spinal fractures. the method used in this research is named the image enhancement method which consists of three sub-methods, namely the enhancement method followed by the sharpening method and the linear contrast stretching method (LCM). Next, we compared and evacuated using Peak signal-to-Noise Ratio (PSNR) and Mean Squared Error (MSE) metrics, comparing the enhanced images to the original scans. Results indicate that linear contrast stretching produces images with darker intensities and a broader intensity range, significantly improving image quality. Notably, the contrast-stretched image numbered ten achieved a PSNR of 22.5122 dB and an MSE of 0.074884, demonstrating superior enhancement. these findings highlight the potential of advanced image processing techniques to substantially improve CT imaging for spinal fracture diagnosis, offering a valuable tool for enhancing clinical outcomes.

关键词： image quality Measurement PSNR Pain Computed tomography Noise Physiology image enhancement signal resolution Medical diagnostic imaging

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Analysis of Malignant Brain Tumor Automated Detection by Artificial Intelligence and MRI Dataset

Analysis of Malignant Brain Tumor Automated Detection by Art...

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signal processing, Communications and Engineering Management conference (ISPCEM), International

作者： Jason Wang Srilaya Bhavaraju Middlesex School Concord Massachusetts United States Massachusetts Institute of Technology Cambridge Massachusetts United States

ISBN: (数字)9798331528676

ISBN: (纸本)9798331528683

A brain tumor is a growth of abnormal cells around the brain. Brain tumors can be benign (not cancerous) or malignant (cancerous). Whether benign or malignant, brain tumors can damage surrounding tissues in the brain. In addition, tumors become increasingly harmful as they grow and their impact can depend on their location. For example, tumors found in the cerebellum can affect movement and tumors found in the optic nerve can affect vision. In this research we explored classifying malignant and benign tumors using artificial intelligence (AI) analysis with CNN, KNN, logistic regression, and stacking model. To accomplish this goal, we analyzed a dataset of MRI images containing images of both malignant and benign brain tumors. Furthermore, we increased the accuracy of malignant brain tumor detection by augmenting this dataset with information about lobe location of the tumors.

关键词： Analytical models Accuracy image color analysis Malignant tumors Magnetic resonance imaging Brain tumors Benign tumors Brain modeling Data models Cancer

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Cognitive Architecture Based Spectrum Sharing between 4G LTE-Advanced Network and TV Band

Cognitive Architecture Based Spectrum Sharing between 4G LTE...

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International conference on Advanced Technologies for signal and image processing (ATSIP)

作者： Hashim Elshafie Naoufel Kraiem Rashid A. Saeed K Saleem A Shahid Khan Mosab Hamdan College of Computer Science King Khalid University Al Farah Abha Saudi Arabia College of Computers and Information Technology(CIT) Taif University Taif Saudi Arabia Center of Excellence in Information Assurance (CoEIA) King Saud University Riyadh Saudi Arabia Faculty of Computer Science and IT Universiti Malaysia Sarawak Kota Samarahan Sarawak Malaysia Walton Institute for Information and Communication Systems Science South East Technological University Waterford Ireland

ISBN: (数字)9798350351484

ISBN: (纸本)9798350351491

there is now a barrier in place because to the exponential increase in demand for faster wireless communications to meet the needs of more applications. there are almost no remaining frequency bands to allot for new systems since the spectral resources are running low. Based on a geographic TV database, this article presents the prospective approach sharing concerns between LTE-Advanced and TV band. the enabling technology will work with LTE-Advanced devices to address the TV band’s under used

关键词： Wireless communication TV Databases image processing White spaces Long Term Evolution

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Recapture Detection to Fight Deep Identity theft 22

Recapture Detection to Fight Deep Identity Theft

引用

Proceedings of the 2022 4th International conference on Video, signal and image processing

作者： Anis Trabelsi Marc Pic Jean-Luc Dugelay Digital Security Department EURECOM France Digital Labs SURYS France

ISBN: (纸本)9781450397810

the progress made in deep learning has allowed the deployment of more powerful biometric authentication systems instead of traditional ones based on passwords or PIN codes. Facial recognition is widely used on smartphones to grant user access. However, advances in deep learning also improve methods for doctoring images and videos. A fraudulent user can use these methods to steal the identity of another person. It is very easy for impostors to present to the smartphone an image or video of the victim's face displayed on another screen. In this paper, we describe the security risks when a facial recognition system is attacked by presenting an image, a video or an interactive deepfake displayed on a screen. We also present a deep learning-based method to detect this kind of attack.

关键词： face anti-spoofing e-KYC recaptured image detection digital image forensics identity theft

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Six Convolutional Layered Deep Convolutional Neural Network Based Real Time Single image Super Resolution

Six Convolutional Layered Deep Convolutional Neural Network ...

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Smart Electronics and Communication (ICOSEC), International conference on

作者： M. Shyamala Devi J. Arun Pandian Rahul Kumar thakur Vinod Babu Vemuri Javvaji Naga Bharath Computer Science & Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamilnadu India School of Information Technology and Engineering Vellore Institute of Technology Vellore India

the super-resolving images investigation has advanced in recent years through the use of cutting-edge Deep Learning-based architectures. Numerous previously documented super-resolution-based solutions need the most advanced and top-tier Graphics processing Units (GPUs) to execute picture super-resolution. With the growing advancement of technology, this research focuses on suggesting the needed quantity of convolutional layers for creating the real time super resolution of single image using Convolutional Neural Network. the proposed Six Convolutional Layered Deep Convolutional Neural Network (6CL-DCNN) to predict the super resolution of images with high accuracy and ideal Peak signal-to-Noise Ratio. the dataset extracted from ***(http://***/Research/Projects/CS/vision/grouping/BSR/BSR_***). the dataset contains 800 images with the combination of low resolution and high-resolution images having the image resolution of 300 * 300 pixels. the proposed 6CL-DCNN built with single input layer followed by six convolutional layers followed by single lambda optimized output layer that predicts the super resolution of both high resolution images and low resolution images. Python was used through 500 training iterations and a 64-bit block size on a NVidia Geforce Tesla V100 GPU workstation. the processed low resolution images and high-resolution images are applied with proposed 6CL-DCNN model and also the performance is compared using Peak signal to Noise Ratio with other optimized output layers. Experimental results shows that the proposed model 6CL-DCNN shows the maximum Peak signal to Noise Ratio of 48 dB when compared to other optimized output layers.

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