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OSEGNET: OPERATIONAL SEGMENTATION NETWORK FOR COVID-19 DETECTION USING CHEST x-RAY imageS 29

OSEGNET: OPERATIONAL SEGMENTATION NETWORK FOR COVID-19 DETEC...

引用

IEEE International conference on image processing (ICIP)

作者： Degerli, Aysen Kiranyaz, Serkan Chowdhury, Muhammad E. H. Gabbouj, Moncef Tampere Univ Fac Informat Technol & Commun Sci Tampere Finland Qatar Univ Dept Elect Engn Doha Qatar

ISBN: (数字)9781665496209

ISBN: (纸本)9781665496209

Coronavirus disease 2019 (COVID-19) has been diagnosed automatically using Machine Learning algorithms over chest x-ray (CxR) images. However, most of the earlier studies used Deep Learning models over scarce datasets bearing the risk of overfitting. Additionally, previous studies have revealed the fact that deep networks are not reliable for classification since their decisions may originate from irrelevant areas on the CxRs. Therefore, in this study, we propose Operational Segmentation Network (OSegNet) that performs detection by segmenting COVID-19 pneumonia for a reliable diagnosis. To address the data scarcity encountered in training and especially in evaluation, this study extends the largest COVID-19 CxR dataset: QaTa-COV19 with 121,378 CxRs including 9258 COVID-19 samples with their corresponding ground-truth segmentation masks that are publicly shared with the research community. Consequently, OSegNet has achieved a detection performance with the highest accuracy of 99.65% among the state-of-the-art deep models with 98.09% precision.

关键词： SARS-CoV-2 COVID-19 Machine Learning Deep Learning

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Hyper Spectral image compression using Higher Order Orthogonal Iteration Tucker decomposition 19

Hyper Spectral Image compression using Higher Order Orthogon...

引用

19th IEEE-India-Council International conference (INDICON)

作者： Sucharitha, B. Sheela, K. Anitha Jawaharlal Nehru Technol Univ Elect & Commun Engn Hyderabad India

ISBN: (纸本)9781665473507

Hyper spectral images need more memory to store, process, and transmit due to the large amount of information they carry. One of the most effective way to compress hyper spectral images is to represent them as a three-dimensional tensor. Tensors are multi-dimensional Structures. Tensors have a wide range of applications in numerical linear algebra, chemometrics, data mining, signal processing, statics, data mining, and machine learning. For dimensional reduction of tensors, many tensor decomposition methods have been developed, which we can adapt to Hyper spectral image compression. The proposed method uses Discrete Wavelet Transform and Higher-Order Orthogonal Iteration Tucker Decomposition method to compress the Hyper spectral image. The simulation results were compared with 3 more tensor decomposition techniques. Four real hyper spectral images were used in the experimentation: Pavia University (610 x 340 x 103), Indian Pines adjusted (145 x 145 x 200), Salinas image ( 512 x 217 x 224), and Abu-beach (150 x 150 x 102). After processing, the perceptional quality of the Hyper spectral images is evaluated using PSNR and SSIM. When compared to the other three methods, the proposed method offers good PSNR and SSIM with High Compression.

关键词： Hyper spectral image Tensor ALS HOSVD HOOI PSNR SSIM

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Research on finite element analysis and signal denoising based on electromagnetic acoustic emission 2

Research on finite element analysis and signal denoising bas...

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2nd International conference on signal image processing and Communication, ICSIPC 2022

作者： Yang, Chenglong Lai, Yushu Li, Qiuyue School of Electronic and information Engineering Chongqing Three Gorges University Chongqing404130 China

ISBN: (纸本)9781510657694

In order to investigate the feasibility of acoustic emission phenomenon aroused by electromagnetic excitation in metal specimens, electromagnetic acoustic emission model based on COMSOL software was established. On this basis, electromagnetic acoustic emission experiments were carried out and real signals with a lot of noise were obtained. To remove the noise of the electromagnetic acoustic emission signal, wavelet packet transform and complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN) was introduced. Firstly, the noise signal of low-frequency was removed by the wavelet packet transform, and the retained node signal was reconstructed. Then the reconstructed signal was resolved by CEEMDAN, and the effective component signal was obtained by the variance contribution rate. Finally, to obtain denoised electromagnetic acoustic emission signal, the effective component signal was reconstructed. The result of simulation and experiment shows that the electromagnetic acoustic emission signal could be effectively obtained by loading electromagnetic excitation, which has greater practical value in engineering applications. © 2022 SPIE.

关键词： Empirical mode decomposition

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Patch Level Segmentation and Visualization of Capsule Network Inference for Breast Metastases Detection 14

Patch Level Segmentation and Visualization of Capsule Networ...

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14th IEEE International conference on signal processing and Communications (SPCOM)

作者： Richa, Malviya Dutta Ahmed, Sekh Arif Dogra, Debi Prosad Dan, Pranab K. IIT Kharagpur RMSoEE Kharagpur W Bengal India XIM Univ Sch CS & Engn Bhubaneswar India IIT Bhubaneshwar Sch Elect Sc Bhubaneswar India

ISBN: (数字)9781665482509

ISBN: (纸本)9781665482509

Capsule Networks are becoming popular for developing AI-guided medical diagnostic tools. The objective of this paper is to carve out a strategy to solve dual problems of classification and segmentation of metastatic tissue regions in one single pipeline. To accomplish this, an attempt has been made in this paper to utilize capsule networks with variational Baye's routing to classify normal and metastatic tissue regions from breast cancer whole slide images. Thereafter, a high-level segmentation of the metastatic tissue region has been carried out using the classified patches. The results obtained with a set of 75,000 patches show that patch-level segmentation is an efficient method to delineate metastatic regions. In the prospect of end-users, visualization of results plays a significant role in selecting the appropriate method for their applications. Capsule networks mimic the way the human brain works. For long, it has been the demand from clinicians that the algorithms used for the automatic classification of cancer pathology should be interpretable. Thus, in clinical practice, such a method will be more acceptable. The efficient region segmentation would aid clinicians in readily demarcating the area of interest and the area of most relevance.

关键词： image segmentation Visualization Pathology Software algorithms Pipelines signal processing algorithms signal processing

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HEIFTS 2-Two New Optical Geometries for Imaging Fourier Transform Spectrometers 25

HEIFTS 2-Two New Optical Geometries for Imaging Fourier Tran...

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conference on Imaging Spectrometry xxV - applications, Sensors, and processing

作者： Horton, Richard F. Ad Hoc Opt Los Lunas NM 87031 USA

ISBN: (纸本)9781510654556;9781510654549

In November 1996, this author presented a SPIE paper on the optical geometry and data reduction scheme for the High Etendue Imaging Fourier Transform Spectrometer, (HEIFTS), a totally new imaging spectrometer with no moving parts. Two more papers followed, demonstrating a working device, and in July 1998 a US Patent #5,777,736 was granted. After a general overview of the original device's optical system and spectral data reduction algorithm, two new optical geometries are presented, the first using two apertures and the second using a "split" pupil. Both new lower f/number geometries require only one beamsplitter to form the image plane interferometer and, as in the original, have two separate image data recording planes, allowing two wavelength ranges for one device or a better signal-To-Noise (SNR) from two arrays in the same band. A very simple high f/number system is proposed which uses only two apertures and two mirrors.

关键词： Imaging arrays Mirrors Beam splitters Fourier transforms image processing Spectrometers Calibration Optical design Spectral data processing Clocks

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Protecting Multiple Types of Privacy Simultaneously in EEG-Based Brain-Computer Interfaces

Protecting Multiple Types of Privacy Simultaneously in EEG-B...

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2024 IEEE International conference on Systems, Man, and Cybernetics, SMC 2024

作者： Meng, Lubin Jiang, xue Jia, Tianwang Wu, Dongrui School of Artificial Intelligence and Automation Huazhong University of Science and Technology Key Laboratory of Image Processing and Intelligent Control Wuhan China

ISBN: (纸本)9781665410205

A brain-computer interface (BCI) enables direct communication between the brain and an external device. Electroencephalogram (EEG) is the preferred input signal in non-invasive BCIs, due to its convenience and low cost. EEG-based BCIs have been successfully used in many applications, such as neurological rehabilitation, text input, games, and so on. However, EEG signals inherently carry rich personal information, necessitating privacy protection. This paper demonstrates that multiple types of private information (user identity, gender, and BCI-experience) can be easily inferred from EEG data, imposing a serious privacy threat to BCIs, To address this issue, we design perturbations to convert the original EEG data into privacy-protected EEG data, which conceal the private information while maintaining the primary BCI task performance. Experimental results demonstrated that the privacy-protected EEG data can significantly reduce the classification accuracy of user identity, gender and BCI-experience, but almost do not affect at all the classification accuracy of the primary BCI task, enabling user privacy protection in EEG-based BCIs. © 2024 IEEE.

关键词： Differential privacy

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Benchmarking and Testing of Qualcomm Snapdragon System-on-Chip for JPL Space applications and Missions

Benchmarking and Testing of Qualcomm Snapdragon System-on-Ch...

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2022 IEEE Aerospace conference, AERO 2022

作者： Towfic, Zaid Ogbe, Dennis Sauvageau, Joe Sheldon, Douglas Jongeling, Andre Chien, Steve Mirza, Faiz Dunkel, Emily Swope, Jason Ogut, Mehmet Cretu, Vlad Pagnotta, Chris California Institute of Technology Jet Propulsion Laboratory 4800 Oak Grove Dr. PasadenaCA91109 United States Qualcomm Technologies Inc. 5775 Morehouse Drive San DiegoCA92121 United States

ISBN: (数字)9781665437608

ISBN: (纸本)9781665437608

As some space missions become more challenging due to new environments, greater distances, or more limited size, weight, and power (SWaP) constraints, spacecraft avionics must adapt to allow the spacecraft to be more autonomous and agile-eliminating the Spacecraft-Earth-Spacecraft feedback loop whenever possible. Prime examples of such missions include Aerobots (such as Ingenuity with extremely low SWaP constraints and demanding signal/image processing during flight) and landers in possibly hostile environments (such as a Europa lander mission, with limited communication capacity, high latency, and constrained power budget). To address these challenges, JPL worked with Qualcomm to demonstrate the use of their Snapdragon 801 system-on-chip (SoC) onboard the Ingenuity Helicopter on Mars. The Qualcomm Snapdragon SoC contains various subsystems, including an ARM cluster, a Graphics processing unit, a Digital signal processing subsystem, a Neural processing Engine, image signal processing subsystem, among others. Since the success of Ingenuity, JPL is continuing to work with Qualcomm to address other applications of the Snapdragon SoC technology. This includes the deployment of two 855 Snapdragon development boards onboard the International Space Station (ISS) for successful in-situ benchmarking of applications in space (beyond those tested on Ingenuity). In this paper, we will examine the performance of various applications that have been identified to benefit from greater onboard computational capability. These applications include (among others): machine vision algorithms that are expected to be critical in autonomous entry-descent-and-landing scenarios and real-time Aerobot flight navigation;Hyperspectral compression algorithms;Synthetic Aperture Radar processing along with various instrument processing algorithms. We discuss how the infusion of Qualcomm's Snapdragon SoC is capable of enabling missions that may not have been able to achieve their goals with tradition

关键词： System-on-chip

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U-Net-based COVID-19 CT image Semantic Segmentation: A Transfer Learning Approach 7

U-Net-based COVID-19 CT Image Semantic Segmentation: A Trans...

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7th International conference on image and signal processing and their applications, ISPA 2022

作者： Ferdi, Abdesselam Benierbah, Said Ferdi, Youcef Frères Mentouri Constantine 1 University Electronics Department Constantine Algeria National Higher School of Biotechnology Biotechnology Department Constantine Algeria

ISBN: (纸本)9781665480420

Deep learning (DL) algorithms are widely applied in many disciplines such as medical imaging, bioinformatics, and computer vision. DL models have been used in medical imaging to perform image segmentation, classification, and detection. During the outbreak of the COVID-19 pandemic, DL has been extensively used to develop COVID-19 screening systems. The reverse transcription-polymerase chain reaction (RT-PCR) is the gold standard method for COVID-19 screening. However, DL has been proposed to detect patients infected with COVID-19 through radiological imaging in Chest x-rays and chest computed tomography (CT) images. This paper proposes transfer learning to train modified U-Net models to segment the COVID-19 chest CT images into two regions of lung infection (ground-glass and consolidation). The proposed modified U-Net models were constructed by replacing the encoder part with a pre-trained convolutional neural network (CNN) model. Three pre-trained CNN models, namely, EfficientNet-b0, EfficientNet-b1, and EfficientNet-b2 were used. The proposed models were evaluated on the COVID-19 CT images Segmentation dataset available in an open Kaggle challenge. The obtained results show that the proposed EfficientNet-b2_U-Net model yielded the highest FScore of 0.5666. © 2022 IEEE.

关键词： COVID-19

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Detection of Internal Defects Based on Empirical Mode Decomposition and wavelets Denoising 2

Detection of Internal Defects Based on Empirical Mode Decomp...

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2nd IEEE Industrial Electronics and applications conference (IEACon)

作者： Cheng, Zhiyuan Zhu, Kaixiong Li, xinghui Qian, xiang Tsinghua Univ Tsinghua Shenzhen Int Grad Sch Shenzhen Peoples R China

ISBN: (纸本)9781728192536

The internal defects of industrial components such as magnetic tiles seriously affect their performance. With the development of intelligent manufacturing technology, industrial manufacturing enterprises need an automatic method to efficiently and accurately detect the internal defects of magnetic tiles. In this paper, a signal pre-processing algorithm based on Empirical Mode Decomposition (EMD) and wavelets denoising is proposed for echo signals for defect detection. Then the variance curve and the adaptive processing method are used to locate the defects accurately. The experimental results show that the algorithm proposed in this paper can been successfully used in defect specimen with different transducer frequency, different defect size and different defect depth. Compared with the original B-scan image, and the internal defects of the specimen could be detected more prominently in enhanced B-scan image, and the accuracy of the defect depth could reach 98.76%, which is better than existing state of the art. Thus, the proposed method has been proved to be effective for optimizing ultrasonic B-mode scanning and accurately locating defects inside magnetic tiles.

关键词： internal defect detection magnetic tile EMD Wavelet soft threshold

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Empirical Analysis of Hybrid Classical Variational Quantum Neural Networks for Target Classification from SAR Data 4th

Empirical Analysis of Hybrid Classical Variational Quantum ...

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4th International conference on Machine Intelligence and signal processing, MISP 2022

作者： Gajjar, Pranshav Saxena, Aayush Ranpariya, Divyesh Shah, Pooja Das, Anup Institute of Technology Nirma University Ahmedabad India SRM Institute Of Science and Technology Chennai India Space Applications Centre ISRO Ahmedabad India

ISBN: (纸本)9789819900848

The advancements in the field of quantum machine learning and the ability of quantum-enhanced networks to acquire a higher effective dimension are promising for remote sensing applications and synthetic aperture radar (SAR) data. This paper aims to incorporate variational quantum circuits with existing classification-based deep learning state-of-the-art architectures. Tests are proposed by using ResNets, DenseNets, and a SqueezeNet-based convolutional neural network. Various dressed quantum circuits associated with these encoders are created and tested for novel applicability analysis. A famous publicly available x-Band SAR image-based dataset called moving and stationary target acquisition and recognition (MSTAR) is utilized to demonstrate image-centric target classification. The utility of quantum-enhanced and hybrid classical-quantum transfer learning models is provided. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Synthetic aperture radar

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