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Pneumonia detection in x-ray chest images based on convolutional neural networks and data augmentation methods 24

Pneumonia detection in X-ray chest images based on convoluti...

引用

24th IEEE conference on signal processing: Algorithms, Architectures, Arrangements, and applications (IEEE SPA)

作者： Garstka, Jakub Strzelecki, Michal Lodz Univ Technol Inst Elect Wolczanska 211-215 PL-90924 Lodz Poland

ISBN: (纸本)9788362065394

Artificial intelligence is gaining in importance in our everyday lives. Convolutional neural networks (CNN) are a very promising and perspective technology in the area of medical images processing, where it could contribute to diagnostics becoming easier and more reliable. Accurate diagnosis is an important factor in the selection of proper and effective treatment. In this paper, a self-constructed convolutional neural network trained on a relatively small dataset for classification of lung x-ray images is presented. This CNN enables classification into one of three categories: healthy, those with bacterial pneumonia, and those with viral pneumonia. Such classification, that considers pneumonia distinction, is rather uncommon among scientific publications. Also, a comparative analysis of the degree of impact of data augmentation on the model's performance and prevention of overfitting was performed. The obtained accuracy of the categorical classification has reached the level of 85% while the sensitivity was equal 0.95. Such results are promising for further work and improvement.

关键词： Pneumonia Convolutional neural network image classification Data augmentation

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MOGA: SEARCHING BEYOND MOBILENETV3

MOGA: SEARCHING BEYOND MOBILENETV3

引用

IEEE International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Chu, xiangxiang Zhang, Bo xu, Ruijun Xia AI Lab Ann Arbor MI 48109 USA

ISBN: (纸本)9781509066315

The evolution of MobileNets has laid a solid foundation for neural network applications on mobile end. With the latest MobileNetV3, neural architecture search again claimed its supremacy in network design. Unfortunately, till today all mobile methods mainly focus on CPU latencies instead of GPU, the latter, however, is much preferred in practice because it has faster speed, lower overhead and less interference. Bearing the target hardware in mind, we propose the first Mobile GPU-Aware (MoGA) neural architecture search in order to be precisely tailored for real-world applications. Further, the ultimate objective to devise a mobile network lies in achieving better performance by maximizing the utilization of bounded resources. Urging higher capability while restraining time consumption is not reconcilable. We alleviate this tension by weighted evolution techniques. Moreover, we encourage increasing the number of parameters for higher representational power. With 200x fewer GPU days than MnasNet, we obtain a series of models that outperform MobileNetV3 under the similar latency constraints(1).

关键词： Neural Architecture Search image Classification Mobile GPU-Awareness Deep Learning

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Theoretical Analysis of Quaternion image Matching Performance

Theoretical Analysis of Quaternion Image Matching Performanc...

引用

conference on Mobile Multimedia/image processing, Security, and applications

作者： DelMarco, Stephen P. BAE Syst 600 Dist Ave Burlington MA 01803 USA

ISBN: (纸本)9781510635760;9781510635753

Recently, quaternions have been applied to multi-channel image processing such as color image registration, edge detection, and image matching. The general approach consists of using up to four image components as separate components in a quaternion-valued image. Techniques from quaternion algebra and analysis, such as quaternion Fourier transforms and correlation, are exploited to implement the desired image processing operations. The motivation underlying use of quaternions involves the concept of holistic data processing. That is, information in separate channels is processed jointly, thereby leveraging correlations across channel. Furthermore, quaternion transforms, such as the Fourier and wavelet transforms, have been defined and can be applied to process quaternion-valued imagery. For example, analogues from classical signal processing, such as the convolution theorem, have been derived and can be used to reduce computational burden. Such quaternion-based image processing techniques can provide performance superior to classical approaches which process image components independently. However, under certain conditions, performance of quaternion-based methods fails to meet performance of independent channel processing methods. In this paper, we perform a theoretical analysis of the performance of quaternion image matching. We present conditions under which quaternion-based processing outperforms, or falls short of, independent channel processing methods. We produce numerical image matching results validating the theoretical performance analysis using synthetic data. We define and solve a linear system of equations to generate the synthetic data. Receiver Operating Characteristics curves are provided to demonstrate the improved performance of the quaternion image matching approach under the favorable conditions predicted by the theory.

关键词： hypercomplex quaternions correlation multi-channel image matching

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Optimal Restoration of Multiple signals in Quaternion Algebra

Optimal Restoration of Multiple Signals in Quaternion Algebr...

引用

conference on Mobile Multimedia/image processing, Security, and applications

作者： Grigoryan, Artyom M. Agaian, Sos S. Univ Texas San Antonio San Antonio TX 78249 USA CUNY CSI New York NY 10021 USA

ISBN: (纸本)9781510635760;9781510635753

This paper offers a new multiple signal restoration tool to solve the inverse problem, when signals are convoluted with a multiple impulse response and then degraded by an additive noise signal with multiple components. Inverse problems arise practically in all areas of science and engineering and refers to as methods of estimating data/parameters, in our case of multiple signals that cannot directly be observed. The presented tool is based on the mapping multiple signals into the quaternion domain, and then solving the inverse problem. Due to the non-commutativity of quaternion arithmetic, it is difficult to find the optimal filter in the frequency domain for degraded quaternion signals. As an alternative, we introduce an optimal filter by using special 4 x4 matrices on the discrete Fourier transforms of signal components, at each frequency-point. The optimality of the solution is with respect to the mean-square-root error, as in the classical theory of the signal restoration by the Wiener filter. The Illustrative example of optimal filtration of multiple degraded signals in the quaternion domain is given. The computer simulations validate the effectiveness of the proposed method.

关键词： Inverse problem of optimal filtration Wiener filter signal restoration quaternion linear convolution quaternion discrete Fourier transform

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Natural image Deblurring using Recursive Deep Convolutional Neural Network (R-DbCNN) and Second-Generation wavelets

Natural Image Deblurring using Recursive Deep Convolutional ...

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IEEE International conference on signal and image processing applications (IEEE ICSIPA)

作者： Khmag, Asem Kamarudin, Noraziahtulhidayu Univ Zawia Dept Comp Syst Engn Fac Engn Az Zawia Libya Univ Coll Technol Sarawak Sch Comp & Creat Sarawak Malaysia

ISBN: (纸本)9781728133775

Deconvolution in blind digital images is a common issue in image enhancement techniques, which basically was a notion of many researches. In this study, spatial varying blind deconvloution is stated and implemented. In addition, image noise removal approach which utilizes the normalized platform of second-generation wavelet transform is applied as pre-processing step. The low and high frequencies are decomposed in this step in order to be extracted. Practically, the main merit of wavelet transform is its efficiency in reduction of data redundancy in digital images. This feature helps a lot in terms of data classification where it is easy to distinguish the signal from its noisy counterpart. The second step, a recursive deep convolutional neural network (R-DbCNN) is implemented to suppress any image blur affected by second-generation wavelet transform to further remove the blur of noisy image. The experimental results depict that the suggested method outperforms recent blur removal techniques for different bluer image types in terms of image quality and time consumption.

关键词： image deblurring second-generation wavelet transforms image enhancement convolution neural network image denoising

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Deep Learning Based Gun Classification in x-Ray images 28

Deep Learning Based Gun Classification in X-Ray Images

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28th signal processing and Communications applications conference, SIU 2020

作者： Karakaya, Ismail Gztiirk, Orkun Bal, Murat Esin, Yunus Emre Havelsan AS. Ankara Turkey

ISBN: (纸本)9781728172064

In this study, deep learning based methods, which classifies the x-Ray images according to the presence of weapon, is proposed. In the first method the classification network has been trained with previously separated training images. In the second method, regions with weapons in the training images were cropped from the image. The training network was run with cropped images. In the third method, training was made with the regions determined in the images by Selective Search. In the last method, the training images were first given as an input image to the Faster R-CNN object detection method. Then, the regions determined as weapons were clipped from the image and included in the training set. Updated training set is given to the classification network. When the proposed four methods were compared, it was seen that the success was higher in the model in which the Faster R-CNN result was included in the training and test steps. © 2020 IEEE.

关键词： Classification (of information)

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Comprehensive Assessment of image Compression Algorithms 43

Comprehensive Assessment of Image Compression Algorithms

引用

conference on applications of Digital image processing xLIII

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

ISBN: (数字)9781510638273

ISBN: (纸本)9781510638273

JPEG image coding standard has been a dominant format in a wide range of applications in soon three decades since it has been released as an international standard. The landscape of applications and services based on pictures has evolved since the original JPEG image coding algorithm was designed. As a result of progress in the state of the art image coding, several alternative image coding approaches have been proposed to replace the legacy JPEG with mixed results. The majority among them have not been able to displace the unique position that the legacy JPEG currently occupies as the ubiquitous image coding format of choice. Those that have been successful, have been so in specific niche applications. In this paper, we analyze why all attempts to replace JPEG have been limited so far, and discuss additional features other than compression efficiency that need to be present in any modern image coding algorithm to increase its chances of success. Doing so, we compare rate distortion performance of state of the art conventional and learning based image coding, in addition to other desired features in advanced multimedia applications such as transcoding. We conclude the paper by highlighting the remaining challenges that need to be overcome in order to design a successful future generation image coding algorithm.

关键词： image compression lossy compression deep learning perceptual visual quality JPEG standard

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Onboard signal processing system of HAYABUSA2 28

Onboard signal processing system of HAYABUSA2

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conference on Infrared Remote Sensing and Instrumentation xxVIII held at SPIE Optical Engineering + applications conference

作者： Hihara, Hiroki Sano, Junpei Takada, Jun Masuda, Tetsuya Okada, Tatsuaki Ogawa, Naoko Ootake, Hisashi Tsuda, Yuichi NEC Space Technol Ltd 1-10 Nisshin Cho Fuchu Tokyo 1838551 Japan NEC Corp Ltd Nakahara Ku 1753 Shimonumabe Kawasaki Kanagawa 2118666 Japan NEC Corp Ltd 1-10 Nisshin Cho Fuchu Tokyo 1838501 Japan Japan Aerosp Explorat Agcy JAXA Chuo Ku 3-1-1 Yoshino Dai Sagamihara Kanagawa 2525210 Japan

ISBN: (纸本)9781510638112;9781510638105

HAYABUSA2 asteroid probe has completed its mission successfully in the vicinity of asteroid Ryugu on November 13, 2019. It is on its way to the Earth now. Digital Electronics and Optical Navigation Camera (DE-ONC) was developed for scientific observation and real-time image recognition for optical navigation. The development process and its high-speed wire rate signal processing architecture of onboard electronics are explained in this lecture. Highly efficient lossless and lossy image compression algorithm were developed to send observed images through within the limited capacity of communication channels between the asteroid Ryugu and the Earth for scientific purposes. Onboard sensitivity and distortion correction functions for image sensors were also developed to improve compression ratio of images. High level synthesis technology was employed to implement the image recognition functions for optical navigation functions into limited numbers of space grade field programmable gate arrays (FPGAs) and to achieve wire rate signal processing speed. It must also satisfy high reliability and safety requirements of HAYABUSA2 missions. Functional distribution mode, standby redundancy mode and hot redundancy mode were realized with the same device configuration. Model based design was performed to satisfy these requirements. The onboard image processing unit of DE-ONC adopts a unified language processing system and a distributed memory model with reference to a parallel inference machine developed for the Fifth Generation Computer Systems aiming at artificial intelligence technology development. Its image processing module integrates a radiation hardened micro-controller unit (MCU) and FPGAs with the unified language processing system and the distributed object model.

关键词： Infrared sensor data compression image recognition optical navigation artificial intelligence Internet of Thing (IoT) SpaceWire digital development

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Determination of Foveal Avascular Zone Parameters Using a New Location-Aware Deep-Learning Method.

Determination of Foveal Avascular Zone Parameters Using a Ne...

引用

conference on applications of Machine Learning

作者： Sobhaninia, Zahra Danesh, Hajar Kafieh, Rahele Balaji, J. Jothi Lakshminarayanan, Vasudevan Isfahan Univ Technol Dept Elect & Comp Engn Esfahan Iran Islamic Azad Univ Dept Biomed Engn Sci & Res Branch Tehran Iran Isfahan Univ Med Sci Med Image & Signal Proc Res Ctr Sch Adv Technol Med Esfahan Iran Med Res Fdn Dept Optometry Chennai 600006 Tamil Nadu India Univ Waterloo Sch Optometry & Vis Sci Theoret & Expt Epistemol Lab Waterloo ON N2L 3G1 Canada

ISBN: (数字)9781510645257

ISBN: (纸本)9781510645257;9781510645240

The Foveal Avascular Zone (FAZ) is of clinical importance since the retinal vascular arrangement around the fovea changes with retinal vascular diseases and in high myopic eyes. Therefore, it is important to segment and quantify the FAZs accurately. Using a novel location-aware deep learning method the FAZ boundary was segmented in en-face optical coherence tomography angiography (OCTA) images. The FAZ dimensions were compared the parameters determined using four methods: (1) device in-built software (Cirrus 5000 Angioplex), (2) manual segmentation using image J software by an experienced clinician, and (4) the new method (new location-aware deep-learning method). The parameters were measured from OCTA data from healthy subjects (n=34) and myopic patients (n=66). For this purpose, FAZ location was manually delineated in en-face OCTA images of dimensions 420x420 pixels corresponding to 6mm x 6mm. A modified UNet segmentation with an additional channel from a Gaussian distribution around the likely location of the FAZ was designed and trained using 100 manually segmented OCTA images. The predicted FAZ and the related parameters were then obtained using a test dataset consisting of 100 images. For analysis, two strategies were applied. The segmentation of FAZ was compared using the Dice coefficient and Structural Similarity Index (SSIM) to determine the effectiveness of the proposed deep learning method when compared to the other three methods. Furthermore, to provide deeper insight, a set of FAZ dimensions namely area, perimeter, circularity index, eccentricity, perimeter, major axis, minor axis, inner circle radius, circumcircle radius, the maximum and minimum boundary dimensions, and orientation of major axis were compared between the 3 methods. Finally, vessel-related parameters including tortuosity, vessel diameter index (VDI) and vessel avascular density (VAD) were calculated and compared. The high myopic eyes exhibited a narrowing the FAZ area and perimeter. The

关键词： Deep-Learning Automated Measurement Foveal Avascular Zone Myopia Optical Coherence Tomography Angiography image processing UNet

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3D-CNN Autoencoder for Plenoptic image Compression

3D-CNN Autoencoder for Plenoptic Image Compression

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IEEE International conference on Visual Communications and image processing (VCIP)

作者： Zhong, Tingting Jin, xin Tong, Kedeng Tsinghua Shenzhen Int Grad Sch Shenzhen Key Lab Broadband Network & Multimedia Shenzhen 518055 Peoples R China

ISBN: (纸本)9781728180687

Recently, plenoptic image has attracted great attentions because of its applications in various scenarios. However, high resolution and special pixel distribution structure bring huge challenges to its storage and transmission. In order to adapt compression to the structural characteristic of plenoptic image, in this paper, we propose a Data Structure Adaptive 3D-convolutional(DSA-3D) autoencoder. The DSA-3D autoencoder enables up-sampling and down-samping the sub-aperture sequence along the angular resolution or spatial resolution, thereby avoiding the artifacts caused by directly compressing plenoptic image and achieving better compression efficiency. In addition, we propose a special and efficient S quare rearrangement to generate sub-aperture sequence. We compare Square with Zigzag sub-aperture sequence rearrangements, and analyzed the compression efficiency of block image compression and whole image compression. Compared with traditional hybrid encoders HEVC, JPEG2000 and JPEG PLENO(WaSP), the proposed DSA-3D(Square) autoencoder achieves a superior performance in terms of PSNR metrics.

关键词： Plenoptic image compression Autoencoder network 3D-CNN

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