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2nd International conference on Intelligent Communication and Computational Techniques (ICCT)

作者： Nayak, Satabdi Kumar, Sanidhya Jangid, Mahesh Manipal Univ Jaipur Dept Informat Technol Jaipur Rajasthan India Manipal Univ Jaipur Dept Comp Sci Jaipur Rajasthan India

ISBN: (纸本)9781728117119

With about 200 million global instances and over 400,000 fatalities a year, malaria continues an enormous strain on global health. Modern information technology plays a major part in many attempts to combat the disease, along with biomedical research and political efforts. In specific, insufficient malaria diagnosis was one of the obstacles to a promising mortality decrease. The paper offers an outline of these methods and explores present advancement in the field of microscopic malaria detection and we have ventured into utilization of deep learning for detection of Malaria Parasite. Deep Learning over the years has proven to be much faster and much more accurate as it automates feature extraction of the dataset. In this research paper, we investigated various models of Deep Learning and monitored which of these models provided a better accuracy and faster resolution than previously used deep learning models. Our results show that Resnet 50 model gave the highest accuracy of 0.975504.

关键词： Malaria detection Deep learning Blood Cell detection Medical image processing

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Brain Tumor Segmentation by Texture Feature Extraction with the parallel Implementation of Fuzzy C-Means using CUDA on GPU 5

Brain Tumor Segmentation by Texture Feature Extraction with ...

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5th IEEE International conference on parallel, distributed and Grid Computing (PDGC)

作者： Saxena, Sanjay Shama, Suraj Int Inst Informat Technol Bhubaneswar IIIT Bhuban Dept Comp Sci & Engn Bhubaneswar India

ISBN: (纸本)9781728106465

Exact segmentation of the brain tumor is one of the imperative tasks in medical image processing and its analysis as it deals with extracting the information of the tumorous region from the brain MRI sequences. Automated segmentation and detection of brain tumors from the brain MRI is an exigent issue caused by the texture, size, shape, and location. In this paper, a significant method of brain tumor segmentation from the FLAIR MRI sequences is by classifying local window followed by parallel fuzzy c means clustering. Fuzzy c- means methods have shown their efficiency in extracting a variety of objects in several medical image processing applications. However, one of the major issues of these algorithms is high computational requirements at the time of dealing with large data set. Nowadays, NVIDIA's GPU plays an extremely essential role in implementing such time-consuming algorithms to reduce the time complexity. Our experiments based on NCI-MICCAI BRATS 2017 FLAIR MRI of HGG (High-Grade Glioma) demonstrate the efficiency of the implemented parallel algorithm. For the segmentation of tumorous region, a mechanism of sliding window is implemented on CPU (host) in which a 45 X 45 sized window is taken to classify whether that particular window is having tumor region or not. For perfect segmentation at the GPU (device) side, fuzzy c means technique is used to get the exact location of the tumor. Approx 17.6 speed up obtained, for the BRATS data sets over the implementation of the algorithm on CPU. Apart from speed up significant dice similarity coefficients are obtained which shows the efficient segmentation in the reasonable time.

关键词： Brain Tumor Segmentation Computed Unified Device Architecture Graphics processing Unit High Performance Computing Texture Feature Extraction Fuzzy C-means

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Rank differences of signals by weighing-selection processing method for implementation of multifunctional image processing processor 4

Rank differences of signals by weighing-selection processing...

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conference on Emerging Imaging and Sensing Technologies for Security and Defence IV part of SPIE Security + Defence Symposium

作者： Krasilenko, Vladimir G. Lazarev, Alexander A. Nikitovich, Diana V. Vinnitsa Social Econ Inst 86-131 Kelestskaya Str UA-21021 Vinnitsa Ukraine Vinnitsa Natl Tech Univ Vinnytsia Ukraine

ISBN: (数字)9781510630307

ISBN: (纸本)9781510630307;9781510630291

In this paper, we proposed a new iterative process of sorting an array of signals, which differs from the known structures of sorting signals by uniformity, versatility, which allows direct and inverse sorting of an array of analog or digital signals. The basic elements of the proposed sorting structures are simple relational nodes. Such elements can be implemented on a different element basis, including, on devices for selecting a maximum or minimum of two analog or digital signals, which are implemented on CMOS current mirrors and carry out the limited difference function of continuous logic. We offered implementation of homogeneous sorting structure on such elements, consisting of two layers and a multichannel sampling and holding device. Nine signals corresponding to a selection window of a matrix sensor are fed to this structure, we sort them in five iterative steps, and at the output we receive the signals sorted by the rank, which, using the code controlled programmable multiplexer, generates an output signal, corresponding to the selected rank. We evaluated the technical parameters of such a relational preprocessor. The base cells consist of no more than 20 CMOS 1.5 mu m transistors, the total power consumption of the sorting node on 10 continuously logical base cells (CL BC) is 2mW, the supply voltage is 1.8 divided by 3.3V, the range of an input photocurrent is 0.1o24 mu A, the conversion cycle is 10 mu s. The paper considers results of design and modeling of CL BC based on current mirrors (CM) for creating picture type image processors (IP) with matrix parallel inputs-outputs. Such sorting nodes based on them have a number of advantages: high speed and reliability, simplicity, small power consumption, high integration level. The inclusion of an iterative node for sorting signals into a modified nonlinear IP structure makes it possible to significantly simplify its design and increase the functional capabilities of such processor. The simulation results co

关键词： analog relational preprocessor image nonlinear processing simulation continuous logic current mirror sorting networks methods of selection and rank preprocessing rank filtering

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End-to-end Spatial Attention Network with Feature Mimicking for Head Detection

End-to-end Spatial Attention Network with Feature Mimicking ...

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International conference on Automatic Face and Gesture Recognition

作者： Junjie Zhang Yuntao Liu RongChun Li Yong Dou National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha Hunan China

ISBN: (数字)9781728130798

ISBN: (纸本)9781728130804

Human head detection is a widely used task and suitable for identifying persons in practical applications. Although existing methods have achieved significant progress, the problems of false alarm and miss detection are still challenging, which arise from weak classification power of detector in the face of variability in occlusion, illumination, etc. In this paper, we present an effective end-to-end head detector called Spatial Attention Network with feature Mimicking(SANM) that can obtain better feature and enhanced classification power, through attention mechanism and a feature mimic method. The spatial-wise attention is extracted from several levels of feature and supervised by the bounding-box annotated heat map. The attention improves the quality of the features in the head and opposite area. To further improve the classification ability, we utilize the feature mimicking method to drive network learning the feature refined by a deep cascading classifier. Compared with the baseline model, our method achieves better performance and produces leading results on head detection benchmarks.

关键词： Feature extraction Detectors Proposals Head Object detection Training Task analysis

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Extraction of Features from Breast Cancer Mammogram image Using Some image processing Techniques 5

Extraction of Features from Breast Cancer Mammogram Image Us...

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5th IEEE International conference on parallel, distributed and Grid Computing (PDGC)

作者： Pati, Dakshya Prasad Panda, Sucheta Veer Surendra Sai Univ Technol Dept Comp Applicat Burla 768018 Odisha India

ISBN: (纸本)9781728106465

Breast cancer is a major root cause of death in most cancer affected women and every year millions of women are dying around the globe because of this deadly disease.. Now-a-days various image processing techniques are used to extract the features from mammogram image. Mammograms are X-ray images of the breast which are used to visualize the early sign of breast cancer. Oncologists are using various images processing technique for effective decisions making, by comparing the mammographic images. Microcalcifications are small calcium deposits that look like white specks on mammogram. These are usually not a result of cancer but if they appear in certain pattern and cluster together, can lead to early stage of cancer. Different image processing technique can be used for detecting these micro calcification. The aim of the paper is to detect the cancerous cells by the use of different existing image filter with their comparisons of output performance and the advantages of this method is future predictions of breast cancer. If the cancer cells are detected at early stage, many precious lives can be saved.

关键词： Breast Cancer image processing Mammograms image Filter

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Reducing Communication in Proximal Newton methods for Sparse Least Squares Problems 18

Reducing Communication in Proximal Newton Methods for Sparse...

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47th International conference on parallel processing (ICPP)

作者： Soori, Saeed Devarakonda, Aditya Blanco, Zachary Demmel, James Gurbuzbalaban, Mert Dehnavi, Maryam Mehri Rutgers State Univ New Brunswick NJ 08901 USA Univ Calif Berkeley Berkeley CA 94720 USA

ISBN: (纸本)9781450365109

Proximal Newton methods are iterative algorithms that solve l1-regularized least squares problems. distributed-memory implementation of these methods have become popular since they enable the analysis of large-scale machine learning problems. However, the scalability of these methods is limited by the communication overhead on modern distributed architecture. We propose a stochastic variance-reduced proximal method along with iteration-overlapping and Hessian-reuse to find an efficient trade-off between computation complexity and data communication. The proposed RC-SFSITA algorithm reduces latency costs by a factor of k without altering bandwidth costs. RC-SFISTA is implemented on both MPI and Spark and compared to the state-of-the-art framework, ProxCoCoA. The performance of RC-SFISTA is evaluated on 1 to 512 nodes for multiple benchmarks and demonstrates speedups of up to 12x compared to ProxCoCoA with scaling properties that outperform the original algorithm.

关键词： Newton-Raphson method

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The Advances and Challenges of Deep Learning Application in Biological Big Data processing

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CURRENT BIOINFORMATICS 2018年第4期13卷 352-359页

作者： Peng, Li Peng, Manman Liao, Bo Huang, Guohua Li, Weibiao Xie, Dingfeng Hunan Univ Sci & Technol Sch Comp Sci & Engn Xiangtan 411201 Hunan Peoples R China Hunan Univ Coll Informat Sci & Engn Changsha 410082 Hunan Peoples R China Shaoyang Univ Coll Informat Engn Shaoyang 422000 Hunan Peoples R China Hunan Ind Polytech Changsha 410208 Hunan Peoples R China

Background: Bioinformatics research comes into an era of big data. Mining potential value in biological big data for scientific research and health care field has the vital significance. Deep learning as new machine learning algorithms, on the basis of big data and high performance distributed parallel computing, show the excellent performance in biological big data processing. Objective: Provides a valuable reference for researchers to use deep learning in their studies of processing large biological data. methods: This paper introduces the new model of data storage and computational facilities for big data analyzing. Then, the application of deep learning in three aspects including biological omics data processing, biological image processing and biomedical diagnosis was summarized. Aiming at the problem of large biological data processing, the accelerated methods of deep learning model have been described. Conclusion: The paper summarized the new storage mode, the existing methods and platforms for biological big data processing, and the progress and challenge of deep learning applies in biological big data processing.

关键词： Deep learning machine learning big data bioinformatics biological image

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LEDNet: Deep Network for Single image Haze Removal 2018

LEDNet: Deep Network for Single Image Haze Removal

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Proceedings of the 11th Indian conference on Computer Vision, Graphics and image processing

作者： Akshay Dudhane Subrahmanyam Murala Abhinav Dhall Computer Vision and Pattern Recognition Lab Indian Institute of Technology Ropar India Learning Affect and Semantic Image Analysis Group Indian Institute of Technology Ropar India

ISBN: (纸本)9781450366151

Haze during the bad weather, degrades the visibility of the scene drastically. Degradation of scene visibility varies with respect to the transmission coefficient/map (Tc) of the scene. Estimation of accurate Tc is key step to reconstruct the haze free scene. Previously, local as well as global priors were proposed to estimate the Tc. We, on the other hand, propose integration of local and global approaches to learn both point level and object level Tc. The proposed local encoder decoder network (LEDNet) estimates the scene transmission map in two stages. During first stage, network estimates the point level Tc using parallel convolutional filters and spatial invariance filtering. The second stage comprises of a two level encoder-decoder architecture which anticipates the object level Tc. We also propose, local air-light estimation (LAE) algorithm, which is able to obtain the air-light component of the outdoor scene. Combination of LEDNet and LAE improves the accuracy of haze model to recover the scene radiance. Structural similarity index, mean square error and peak signal to noise ratio are used to evaluate the performance of the proposed approach for single image haze removal. Experiments on benchmark datasets show that LEDNet outperforms the existing state-of-the-art methods for single image haze removal.

关键词： Haze removal transmission map CNN

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Rapid Face image Retrieval for Large Scale Based on Spark and Machine Learning

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Journal of Physics: conference Series 2021年第1期2026卷

作者： Yuting Zeng Yujin Li Qiming Hu Yuan Tang School of Information Science and Technology Chengdu University of Technology Chengdu 610059 China

This paper proposed a distributed computing environment large-scale face image retrieval method. Based on Spark platform, a face retrieval method based on machine learning is established, which takes advantage of parallel computing to improve the efficiency of face image retrieval. In the method, SIFT algorithm is used for feature coding, PCA dimension reduction processing, HBase database is used for data storage, and KD-Tree query algorithm is used to match images similar to the query images. Meanwhile, large-scale computing engine uses Spark to process data to improve the retrieval efficiency. The CelebA dataset is selected to test the method, and the experimental results show the effectiveness of the method.

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Machine Learning based image processing Techniques for Satellite image Analysis -A Survey

Machine Learning based Image Processing Techniques for Satel...

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International conference on Machine Learning, Big Data, Cloud and parallel Computing (COMITCon)

作者： Anju Asokan J Anitha ECE Department Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (纸本)9781728102122

This paper presents the detailed comparison of various image processing techniques for analyzing satellite images. The satellite images are large in size, acquired from long distances and are affected by noise and other environmental conditions. Hence it is necessary to process them so that they can be used by the researchers for analysis. Satellite images are widely used in many real time applications such as in agriculture land detection, navigation and in geographical information systems. In this paper, a review of some popular machine learning based image processing techniques is presented. Also a detailed comparison of various techniques is performed. Limitations in each image processing method are also described. In addition to reviewing of different methods, different metrics for performance evaluation in each of the image processing areas is studied.

关键词： Feature extraction Satellites image segmentation Classification algorithms image enhancement image fusion

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