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PneumonxNet - A deep learning model for pneumonia detection

PneumonXNet - A deep learning model for pneumonia detection

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signal processing and Advance Research in Computing (SPARC), International conference on

作者： Harshit Aditya Anchita Ramani Nilesh Kumar Kasturi Vandana Bhattacharjee Department of Computer Science and Engineering Sikkim Manipal Institute of Technology Majitar India Department of Computer Science and Engineering Birla Institute of Technology Mesra Ranchi India

ISBN: (数字)9798350385205

ISBN: (纸本)9798350385212

Standard chest x-rays are used to diagnose pneumonia, an infection of the lungs that can be highly contagious due to being caused by viruses and bacteria. Nevertheless, this method is more time-consuming and routine-dependent with a bias in outcome. So to overcome these problem we developed PneumonxNet, a bespoke CNN model for the detection of pneumonia. We evaluated the performance of our method with respect to pre-trained models such as VGG16, EfficientNetB3 and ResNet50 on a x-ray image dataset comprising 5,856 images. The best result we have reached is a test accuracy of 97% with dropout value of 0.4 which is better than the pretrained models. The pre-trained model EfficientNetB3 showed the highest accuracy, which points on possible applications of transfer learning. This research signifies the capabilities of CNN models to streamline and improve detection on pneumonia, which in turn paves way for technological sophistication in health care sectors. The findings of this study carry significant impact on pneumonia detection, emphasizing the importance of deep learning model in pneumonia detection and how it can help in saving a lot of time for doctors and patients from the traditional x rays collecting systems. This can also reduce the overall treatment cost for patients. This research therefore offers new avenues for developing strong techniques for pneumonia identification, which hold promise for improving healthcare technology and patient outcomes in general.

关键词： Deep learning Pneumonia Accuracy Costs Transfer learning Medical services x-ray imaging Biomedical imaging Residual neural networks Testing

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Research on super-resolution reconstruction of single-frame image of infrared focal plane polarization imaging 8

Research on super-resolution reconstruction of single-frame ...

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8th Symposium on Novel Photoelectronic Detection Technology and applications

作者： Yuan, Hongwu Wang, Yiqing xu, Guoming Wang, Feng School of Big Data and Artificial Intelligence Anhui Xinhua University Hefei China Key Laboratory of Polarization Imaging Detection Technology in Anhui Province Hefei China Institute of Intelligent Information Anhui Xinhua University Hefei China School of Internet Anhui University Hefei China

ISBN: (数字)9781510653122

ISBN: (纸本)9781510653115

Since the low resolution of infrared focal plane arrays may degrade the performance of polarization imaging significantly, it is necessary to study the super-resolution reconstruction method for superior image resolution and contrast. Four typical single-frame image reconstruction methods are studied in this paper, and the comparison of reconstruction result of these methods is conducted by using subjective and objective evaluation. The experiments show that the reconstruction method based on generative adversarial network performs poorly in the evaluation indexes such as peak signal-to-noise ratio and structural similarity, but its reconstructed image has good visual effects, rich texture and details, and has a strong ability to suppress background noise, and using the reconstructed images for polarization information parsing can significantly improve the accuracy of polarization information parsing and the effect of polarization image fusion. © 2022 SPIE

关键词： image fusion

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Detail Enhancement and Segmentation Extraction of Cladding image Based on Super Resolution

Detail Enhancement and Segmentation Extraction of Cladding I...

引用

conference on AOPC - Advanced Laser Technology and applications

作者： Tang Mengyu xu Zhenying Wu Ziqian Li Qiling Ling Jun Yan JinJin Jiangsu Univ Sch Mech & Engn Zhenjiang 212013 Jiangsu Peoples R China

ISBN: (数字)9781510649965

ISBN: (纸本)9781510649965;9781510649958

Aiming at the images of relevant monitored objects in the process of laser cladding, a super resolution algorithm technology was proposed to optimize and enhance the key details of the images, and the enhanced image content was segmented, extracted and counted. First, construct a training of a sub-resolution convolutional neural network (SRCNN) model;the original low resolution is predicted by the weight after training, the image quality evaluation results: peak signal-to-noise ratio (PSNR) is 30.198212, structural similarity (SSIM) is 0.969966;the most based on the maximum entropy dual threshold split algorithm combined with image processing, extracting and statistics on the powder object in the segmentation result image, the number of effective powders and proportion of the original wandering map and the predicted output delay image is [112, 33.6%] and [240, 40.6%]. The research results show that the cladding image output from the original image after the super resolution model has been significantly improved in terns of clarity and quality as well as the optimization and enhancement of the details of the monitored object.

关键词： Super Resolution Algorithm Cladding image image Quality Evaluation Segmentation and Extraction Detail Enhancement

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Review of subjective quality assessment methodologies and standards for compressed images evaluation 44

Review of subjective quality assessment methodologies and st...

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conference applications of Digital image processing xLIV

作者： Testolina, Michela Ebrahimi, Touradj Ecole Polytech Fed Lausanne EPFL Multimedia Signal Proc Grp MMSPG CH-1015 Lausanne Switzerland

ISBN: (数字)9781510645233

ISBN: (纸本)9781510645233;9781510645226

Acquiring and exchanging a large number of pictures has become nowadays a common practice. Therefore, new image compression solutions to optimize the storage resources are in constant demand. In this context, it is essential to have a solid methodology to evaluate the performance of compression techniques. Such performance is usually measured through objective quality metrics, which are fast and inexpensive but not always reliable. However, performance is best assessed through subjective image quality assessment experiments, which are expensive and time-consuming, but reliable as based on the subjective opinion of a large number of subjects. These experiments are usually conducted in a controlled laboratory environment, with high-quality monitors and controlled lighting conditions. Recently, encouraged by the COVID-19 pandemic, crowdsourcing-based subjective image quality experiments are gaining popularity, and have demonstrated to be a faster and cheaper alternative to traditional approaches in subjective quality assessment. In this paper, different methodologies for subjective image quality assessment experiments are examined, including a review of the released standards as well as a list of publicly available tools. Moreover, the analysis is extended to novel plenoptic imaging techniques, i.e. point clouds and light fields, and to visually lossless quality assessment approaches. Authors hope that this work will help researchers interested in conducting subjective experiments for assessing the quality of compressed media, to better select the appropriate methodology for their use cases.

关键词： Perceptual quality assessment subjective quality image compression crowdsourcing point clouds light fields visually lossless compression

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processing of the signal Obtained from x-Ray Fluorescence Emissions to Estimate Localization in Biomedical applications

Processing of the Signal Obtained from X-Ray Fluorescence Em...

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Argentine conference on Electronics (CAE)

作者： Nicolas E. Martín Miguel Sofo Haro Nauro Valente LIIFAMIRX IFEG-CONICET & FaMAF-UNC Córdoba Argentina CNEA Reactor Nuclear RAO-UNC IFEG-CONICET & FaMAF-UNC Córdoba Argentina

x-ray fluorescence detection has been widely used in recent years in computed tomography due to its ability to non-destructively acquire structural and elemental information from samples. In this paper, we present a complete irradiation and image reconstruction system based on a linear spatial configuration of cadmium telluride detectors. This configuration is used to analyze the energy spectra detected in a phantom model, which was scanned by Monte Carlo simulation to examine the K series of the emission lines of Au nanoparticles (GNPs). The phantom used in our study has a cylindrical shape with a diameter of 60 mm and a height of 100 mm. It is filled with a material equivalent to water and tissue and contains five embedded cylinders, with concentrations of 1% down to 0.1% GNP. To simulate the irradiation process, we use the PENELOPE Monte Carlo technique tool in conjunction with a 150 kV monoenergetic source. A previously characterized cadmium telluride detector model was used and reconstructed with two widely used techniques in the environment to evaluate the complete system. The results obtained from our study demonstrate that the proposed detector configuration together with the proposed irradiation simulation system produces measurable and viable signals, comparable with other studies carried out, which would allow the effective application of various complex image reconstruction techniques.

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Mapping EEG signals to Visual Stimuli: A Deep Learning Approach to Match vs Mismatch Classification

Mapping EEG Signals to Visual Stimuli: A Deep Learning Appro...

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IEEE International conference on signal processing, Communications and Computing (ICSPCC)

作者： Yiqian Yang Zhengqiao Zhao Qian Wang Yan Yang Jingdong Chen Center of Intelligent Acoustics and Immersive Communications Shaanxi Provincial Key Laboratory of Artificial Intelligence School of Marine Science and Technology Northwestern Polytechnical University Xi'an Shaanxi China Hong Kong University of Science and Technology North Electro-Optic Science & Technology Defense Co. Ltd

ISBN: (数字)9798350366556

ISBN: (纸本)9798350366563

Existing approaches to modeling associations between visual stimuli and brain responses are facing difficulties in handling between-subject variance and model generalization. Inspired by the recent progress in modeling speech-brain response, we propose a “match-vs-mismatch” deep learning model in this study to classify whether a video clip elicits neural responses in recorded EEG signals. Our model employs dilated convolutional neural networks and gated recurrent units to extract features from both EEG and video signals, enabling the learning of associations between visual content and corresponding neural recordings. We demonstrate that our proposed model achieves the highest accuracy on unseen subjects compared to other baseline models. Additionally, we assess inter-subject noise using a subject-level silhouette score in the embedding space, revealing that our model effectively mitigates inter-subject noise and significantly reduces the silhouette score. Furthermore, we investigate Grad-CAM activation scores, revealing that brain regions linked to language processing contribute most to model predictions, followed by regions associated with visual processing. These findings hold promise for advancing neural recording-based video reconstruction and related applications.

关键词： Deep learning Visualization Accuracy Noise Predictive models Brain modeling Feature extraction Electroencephalography Vectors image reconstruction

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Hyperspectral Demosaicing of Snapshot Camera images Using Deep Learning 44th

Hyperspectral Demosaicing of Snapshot Camera Images Using De...

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44th DAGM German conference on Pattern Recognition (DAGM GCPR)

作者： Wisotzky, Eric L. Daudkane, Charul Hilsmann, Anna Eisert, Peter Fraunhofer Heinrich Hertz Inst Comp Vis & Graph Berlin Germany Humboldt Univ Inst Informat Visual Comp Berlin Germany

ISBN: (纸本)9783031167881;9783031167874

Spectral imaging technologies have rapidly evolved during the past decades. The recent development of single-camera-one-shot techniques for hyperspectral imaging allows multiple spectral bands to be captured simultaneously (3 x 3, 4 x 4 or 5 x 5 mosaic), opening up a wide range of applications. Examples include intraoperative imaging, agricultural field inspection and food quality assessment. To capture images across a wide spectrum range, i.e. to achieve high spectral resolution, the sensor design sacrifices spatial resolution. With increasing mosaic size, this effect becomes increasingly detrimental. Furthermore, demosaicing is challenging. Without incorporating edge, shape, and object information during interpolation, chromatic artifacts are likely to appear in the obtained images. Recent approaches use neural networks for demosaicing, enabling direct information extraction from image data. However, obtaining training data for these approaches poses a challenge as well. This work proposes a parallel neural network based demosaicing procedure trained on a new ground truth dataset captured in a controlled environment by a hyperspectral snapshot camera with a 4 x 4 mosaic pattern. The dataset is a combination of real captured scenes with images from publicly available data adapted to the 4 x 4 mosaic pattern. To obtain real world ground-truth data, we performed multiple camera captures with 1-pixel shifts in order to compose the entire data cube. Experiments show that the proposed network outperforms state-of-art networks.

关键词： Sensor array and multichannel signal processing Deep learning Biomedical imaging techniques image analysis

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Learning residual coding for point clouds 44

Learning residual coding for point clouds

引用

conference applications of Digital image processing xLIV

作者： Lazzarotto, Davi Ebrahimi, Touradj Ecole Polytech Fed Lausanne EPFL Multimedia Signal Proc Grp MMSPG Lausanne Switzerland

ISBN: (数字)9781510645233

ISBN: (纸本)9781510645233;9781510645226

Recent advancements in acquisition of three-dimensional models have been increasingly drawing attention to imaging modalities based on the plenoptic representations, such as light fields and point clouds. Since point cloud models can often contain millions of points, each including both geometric positions and associated attributes, efficient compression schemes are needed to enable transmission and storage of this type of media. In this paper, we present a detachable learning-based residual module for point cloud compression that allows for efficient scalable coding. Our proposed method is able to learn the encoding of residuals in any layered architecture, and is here implemented in a hybrid approach using both TriSoup and Octree modules from the G-PCC standard as its base layer. Results indicate that the proposed method can achieve performance gains in terms of rate-distortion when compared to both base layer alone, which is demonstrated both through objective metrics and subjective perception of quality in a rate-distortion framework. The source code of the proposed codec can be found at https://***/mmspg/learned-residual-pcc.

关键词： Point cloud Residual coding Learning-based compression

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Can deep models benefit from standard preprocessing of pulsed thermography data? 47

Can deep models benefit from standard preprocessing of pulse...

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47th International conference on Infrared, Millimeter and Terahertz Waves (IRMMW-THz)

作者： Wei, Z. Osman, A. Mueller, D. Fernandes, H. Tarpani, J. R. Maldague, x. Univ Appl Sci Saarbruecken D-66117 Saarbrucken Germany Fraunhofer Inst Nondestruct Testing IZFP D-66123 Saarbrucken Germany Univ Fed Uberlandia BR-38408100 Uberlandia IL Brazil Univ Sao Paulo BR-04021001 Sao Paulo SP Brazil Univ Laval Quebec City PQ G1V 0A6 Canada

ISBN: (纸本)9781728194271

As deep learning-based algorithms keep leading the data segmentation tasks, many efforts are being conducted for their applications in the processing of pulsed thermography sequences. Conventional data preprocessing methods, such as pulsed phase transformation and principal component analysis are still popular in the community as data enhancement methods to improve contrasts and signal-noise ratio in infrared images. In this contribution, the impact of applying two conventional preprocessing methods for pulsed thermography data on the performance and learning computational time of deep learning methods is evaluated. The presented deep learning methods are based on the Encoder-Decoder architectural form, specifically a U-Net and a SegNet. The results show that by applying the conventional transform, the training time for deep learning methods can be significantly reduced. Compared to the results on the original infrared data, the performance on the preprocessed data is kept relatively high, as demonstrated on polyvinyl chloride specimens.

关键词： Training Deep learning image segmentation Data preprocessing Transforms Data models Task analysis

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KIT: Kernel Isotropic Transformation of Bilateral Filters for image Denoising on FPGA

KIT: Kernel Isotropic Transformation of Bilateral Filters fo...

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International conference on Field Programmable Logic and applications

作者： Fanny Spagnolo Pasquale Corsonello Fabio Frustaci Stefania Perri Department of Informatics Modelling Electronics and Systems Engineering Department of Mechanical Energy and Management Engineering University of Calabria Rende Italy

ISBN: (数字)9798331530075

ISBN: (纸本)9798331530082

A Bilateral filter (BF) is commonly adopted as a pre-processing stage in several computer vision tasks because of its ability to denoise images. In contrast to the traditional image convolution that adopts a static kernel, a BF computes adaptive weights on-the-fly by applying exponentiation and division operations to the current pixel window. Prior works dealing with hardware acceleration of the BF rely on straightforward implementations that approximate the exponential function through look-up-tables (LUTs). This paper presents a new approximation technique to efficiently deploy a BF within real-time and low-energy intelligent systems based on FPGAs. The proposed strategy replaces the adaptive filter with its inexact isotropic version. This choice allows dropping a certain number of operations, thus resulting in enhanced speed and energy performances with respect to state-of-the-art hardware accelerators. When implemented on the AMD xilinx Zynq xC7Z020 FPGA device, the proposed $5 \times 5$ BF design elaborates ∽237 Mega pixels per second and consumes at most 174 mW, with a Peak signal-to-Noise Ratio (PSNR) degradation of just 0.55% at a noise standard deviation equal to 30.

关键词： Performance evaluation PSNR Noise Adaptive filters Real-time systems Table lookup Kernel Field programmable gate arrays Hardware acceleration Standards

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