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Control Instrumentation System Conference (CISCON)

作者： Shlok Mehta Keyur Pancholi Sakshi Pandey Sudhir Dhage Computer Engineering Department Sardar Patel Institute of Technology Mumbai India

ISBN: (数字)9798350375480

ISBN: (纸本)9798350375497

This research paper aims to address the critical need for efficient and accurate identification of chest diseases using chest X-rays through a combination of advanced image processing techniques and machine learning algorithms. With the growing prevalence of respiratory and cardiovascular conditions worldwide, timely and precise diagnosis is paramount for effective patient care. The study begins with a comprehensive review of existing methodologies and technologies employed in the identification of chest diseases from X-ray images. It critically evaluates the strengths and limitations of current approaches, highlighting the challenges faced in achieving high accuracy, speed, and scalability. To address these issues, the project aims to develop an AI-powered system for medical image analysis. In response to these challenges, our research proposes a novel approach that integrates Inception v3 model and imagenet. We leverage a large dataset of annotated chest X-rays to train a deep neural network capable of recognizing subtle patterns indicative of various diseases, including pneumonia, pneumothorax, lung and cardiac abnormalities. The model is optimized to provide not only accurate diagnoses but also to minimize false positives and negatives. In conclusion, this research contributes to the ongoing efforts in utilizing chest X-ray images for disease identification, presenting a robust and efficient methodology that could revolutionize the current diagnostic landscape. The findings hold promise for the development of automated systems capable of assisting healthcare professionals in the accurate and timely detection of chest diseases, ultimately contributing to enhanced patient care and management.

关键词： Accuracy Pneumonia Machine learning algorithms Reviews Scalability Medical services Pattern recognition X-ray imaging Medical diagnostic imaging Diseases

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Electronic Aid Design of Fruits image Classification for visually Impaired People 4th

Electronic Aid Design of Fruits Image Classification for Vis...

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4th International Conference on Information and Communication Technology for Intelligent systems, ICTIS 2020

作者： Srividhya, v. Sujatha, K. Aruna, M. Sangeetha, D. EEE Department Dr. MGR Educational and Research Institute Chennai India EEE Department Meenakshi College of Engineering Chennai India

ISBN: (纸本)9789811570612

Deep learning has attained immense achievement in many fields, such as computer vision and usual speech processing in recent years. It has a burly erudition capability and can create enhanced use of datasets for characteristic extraction when compared to traditional machine learning systems. The researchers start doing research work in this deep learning due to its popularity. In this manuscript, we chiefly initiate several superior neural networks of deep learning and their functions. Deep learning technologies are becoming the major approaches for natural signal and information processing, like image classification, speech recognition. Deep learning is a technology inspired by the functioning of human brain. Convolutional neural networks (CNN) become very popular for image classification in deep learning. CNN’s perform better than human subjects on many of the image classification datasets. In this project, a deep learning convolutional network based on Tensorflow is deployed using python for binary image classification. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Learning algorithms

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Live Demonstration: A Customizable Medical IR Imaging System for Clinical Diagnosis

Live Demonstration: A Customizable Medical IR Imaging System...

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IEEE International Symposium on Circuits and systems (ISCAS)

作者： R. de la Rosa-vidal J. A. Leñero-Bardallo F. J. Garrido-Flores Á. Rodríguez-vázquez J. Bernabéu-Wittel Instituto de Microelectrónica de Sevilla IMSE-CNM (CSIC Universidad de Sevilla) and Virgen del Rocío Hospital Seville Spain

Thermography imaging has great potential for the diagnosis and follow-up of clinical entities that alter body temperature locally [1]. Infrared (IR) cameras have dropped in price during the last ten years. Nowadays, they are compatible with clinical use because they provide immediate results, do not require physical contact with the patient, and their images are easy to interpret. In particular, vascular anomalies constitute a vast family of clinical entities that alter body temperature and can exploit this emerging imaging modality [2]. Many IR systems devoted to the medical field are available in the market. However, to the best of our knowledge, there is a lack of customizable systems easily adaptable to particular medical application scenarios by incorporating different peripherals or sensors and embedding custom image processing algorithms. Commercial IR systems are limited to image acquisition.

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Efficient Framework for Diagnosis of Acute Lymphoblastic Leukemia Using Random Forest Classifier

Efficient Framework for Diagnosis of Acute Lymphoblastic Leu...

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Conference on Modeling of systems Circuits and Devices (MOS-AK India)

作者： Ramya v Karthikeyan B Kalluri Srinath Reddy Jayashree R Harikrishna M Department of ECE MVJ College of Engineering Bengaluru India

ISBN: (数字)9798350395556

ISBN: (纸本)9798350395563

The aberrant growth of white blood cells in the blood and myeloid tissue is the hallmark of leukaemia, which pathologists find it by observing at a blood smear viewed through a microscope. To identify and define leukaemia, pathologists look at the quality of the cells and their physical properties. Microscopic image analysis is crucial for the early leukaemia screening and accurate diagnosis, because current approaches depend on manual inspection, which is tedious and strongly reliant on province specialist's knowledge. Computerized leukaemia diagnosis reveals new possibilities for the eliminating human intrusion while also giving better accurate objective data. This paper provides a framework for automatic identification of acute lymphoblastic leukaemia from outermost blood smear pictures predicted on traditional digital image processing techniques and Machine Learning algorithms. This method of automated leukaemia identification was found to be more effective, fast and precise.

关键词： White blood cells Technological innovation image analysis Accuracy Machine learning algorithms Microscopy Process control

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Crop Selection and Yield Prediction using Machine Learning algorithms

Crop Selection and Yield Prediction using Machine Learning A...

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Augmented Intelligence and Sustainable systems (ICAISS), International Conference on

作者： J. Ashok Kumar N. Parimala R. Pitchai B V Raju Institute of Technology Narsapur India

Agriculture monitoring, particularly in developing nations, can assist prevent famine and aid human efforts. Estimating crop yields before harvest, often known as yield estimation, is difficult. Our technique predicts agricultural yields using publicly available remote sensing data in a scalable manner. Accurate, and economical manner. This strategy might assist all farmers use the least amount of fertilizer feasible, thereby maintaining soil health, while also giving them with the option to make the greatest money from the same piece of land through government-established soil health centers. As a result, it would be a win-win situation for all parties involved. This can be accomplished using machine learning algorithm KNN and image processing. Based on the results of the prediction analysis, machine learning algorithms are utilized to anticipate the best crop as well as the related bio-fertilizer. image processing could be utilized to further improve automated drones or tractors because it generates the shortest number of turns through the field. A predictive study is performed to recommend the top three most suitable crops based on soil nutrient levels, temperature, and possible revenue that this crop could generate. The outcomes can be applied in two ways. The first option is the automatic method, in which the farmer just selects their area and a suitable crop is selected based on prior experiments completed nearby. Second, you can manually enter soil information and get a crop recommendation based on the value you enter.

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Fast guided filter for power-efficient real-time 1080p streaming video processing

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JOURNAL OF REAL-TIME image processing 2020年第3期17卷 511-525页

作者： El Mezeni, Dragomir Saranovac, Lazar Univ Belgrade Sch Elect Engn Blvd Kralja Aleksandra 73 Belgrade Serbia

With the advent of embedded vision systems, smart sensors with integrated image signal processing (ISP) become a hot topic. This poses a need for efficient hardware implementation, regarding resource utilization and power consumption, of core image processing algorithms. Power consumption is especially important, since many of the target devices are usually battery operated. Edge-aware filtering, although it is used in many core image processing algorithms, is still challenging operation, especially in cases where large kernels are needed. In this paper, efficient hardware realization of fast guided filter (FGF) is proposed. It is based on idea that large filter of size R=K.S can be calculated by downsampling input image by factor S and using filter of size K. Besides reduced memory and logic requirements, this optimization enables that, for the scaling factor S, core processing is done at 1/S2 pixel clock, providing significantly lower power consumption. Experimental results on Cyclone v FPGA chip demonstrate that, for FGF of size 35x35 with downsampling factor S=7 the proposed design achieves 60 fps for 1080p video. Memory utilization is 147.3 kB without need for any off-chip memory. Core dynamic power consumption is 79.89 mW. Proposed design consumes less total power than state-of-the-art guided filter realizations including ASIC-based solutions. This module can be seamlessly integrated into smart sensors ISP units, because it is designed for power-efficient streaming processing.

关键词： Guided image filtering Real-time video processing Power-efficient video processing

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Development of a Phasogrammetric Measurement System for Error Estimation in Close-Range Photogrammetry

Development of a Phasogrammetric Measurement System for Erro...

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Wave Electronics and its Application in Information and Telecommunication systems (WECONF)

作者： A. Yu. Poroykov N. Yu. Sivov E. v. Shmatko Department of Physics Named After V.A. Fabrikant National Research University "MPEI" Moscow Russia

In aeronautics, engineering, medicine, robotics and other industries, optical methods are widely used to measure the geometry and surface deformation of various objects from their images. These methods are based on digital image processing algorithms and can be summarized in a group of methods called close-range photogrammetry. When developing photogrammetric measurement systems, one of the problems to be solved is the estimation of their error. In this work, it is proposed to use physical simulation to estimate the error, which gives more reliable results than theoretical calculations or computer simulation. The phasogrammetric method has been chosen as the reference method for surface shape measurement. This paper presents the results of the development of an optical phasogrammetric measurement system based on the structured illumination method and an evaluation of its accuracy characteristics.

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Prediction of Herbs with its Benefits using Deep Learning Techniques

Prediction of Herbs with its Benefits using Deep Learning Te...

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2022 International Conference on Sustainable Computing and Data Communication systems, ICSCDS 2022

作者： Begum, M. Sharmila Haris, R. vetrimaran, v. Raj, P. Ashwin B.Tech Computer Science and Engineering Pmist Tamil Nadu Vallam Thanjavur India

ISBN: (纸本)9781665478847

Automated plant identification is a very promising solution for bridging the taxonomic gap, which is receiving much attention from botany and computer science. As machine learning technology advances, more complex models have been proposed to automate crop identification. Herbal remedies are considered in the pharmaceutical industry due to fewer harmful side effects and less expensive than modern medicine. Based on these data, many researchers have shown great interest in studying the recognition of natural herbal medicines. There are various possibilities for moving towards solid phase production capable of accurately discriminating medicinal plants in real time. In this project, efficient and reliable machine learning algorithms for plant catalogues using leaf images used in recent years are being studied. The review covers image processing techniques used to locate leaves and extract important leaf features from other machine learning steps. These deep learning stages are classified according to their function when it comes to discriminating leaf images based on common plant characteristics, i.e. shapes, ridges, textures and combinations of many elements. Then you get results using herbs with improved accuracy. The test results indicate that the proposed system provides an improved level of accuracy. © 2022 IEEE.

关键词： Deep neural networks

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BISHENG: A 49.38PPM Flexible Multi-Function Printer SoC in 28nm CMOS

BISHENG: A 49.38PPM Flexible Multi-Function Printer SoC in 2...

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IEEE International Conference on Integrated Circuits, Technologies and Applications (ICTA)

作者： Dongliang Xiong Xiaoxu Zhang Siwen Xiu Zhili Liu Dandan Zheng Li Ding Aiguo Yin Peiyong Zhang Kai Huang Zhejiang University Zhejiang Gongshang University China Jiliang University Geehy Semiconductor Pantum Electronics

ISBN: (数字)9798331530723

ISBN: (纸本)9798331530730

Innovative AI functions and imaging algorithms are important for multi-function printers (MFP) to provide good user experience. However, prior printer system on chips (SoC) using hardwired hardware or customized processors have limited flexibility and programmability. This paper proposes a flexible MFP SoC called BISHENG, which uses customized RISC-v processors and a hybrid interconnect with sequential pipeline and partial crossbar. The chip was fabricated in 28 nm CMOS. Experiment result shows that it can achieves 49.38 pages per minute (PPM) in prototype printer with a typical power 2.55 W. Keywords-System on Chip, Multi-Function Printer, Document image processing, On-Chip Network

关键词： Program processors image color analysis Pipelines Integrated circuit interconnections Imaging Prototypes User experience Hybrid power systems System-on-chip Printers

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A MobileUnet++-based Abnormality Segmentation and Multi-scale Network Approach for Brain Tumor Classification

A MobileUnet++-based Abnormality Segmentation and Multi-scal...

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Intelligent algorithms for Computational Intelligence systems (IACIS), International Conference on

作者： veesam Pavan Kumar Satya Ranjan Pattanaik v Sunil Kumar Dept of Computer Science and Engineering Biju Patnaik University of Technology(BPUT) Rourkela India Dept of Computer Science and Engineering Gandhi Institute for Technology(GIFT) Bhubaneswar India Dept of Computer Science and Engineering PBR Visvodaya Institute of Technology & Science Kavali India

ISBN: (数字)9798350360660

ISBN: (纸本)9798350360677

Tumors are a pervasive concern in modern life, driven by cellular irregularities that disrupt the orderly division necessary for healthy cell growth. However, brain tumors present unique challenges compared to tumors in other regions due to the intricacies of brain structure and function. A brain tumor occurs when cells in the cerebral cortex undergo uncontrolled proliferation. Diagnosing brain tumors, especially in their early stages is a formidable task that often relies on manual examination of medical images by radiologists or healthcare professionals. Despite various approaches to brain tumor classification, precision remains paramount while ensuring practical utility in segmentation and classification tasks. Traditional methods are often manual, time-consuming and lacking in automated classification capabilities, hampering efficient decision-making. The advent of deep learning technology has revolutionized medical image processing, particularly in the realm of brain tumor analysis. Deep learning algorithms, powered by vast datasets and computational prowess, offer remarkable capabilities in automating segmentation and classification tasks, thereby enhancing accuracy and efficiency in diagnosing brain tumors. Consequently, this study introduces a method for smart brain tumor segmentation and classification leveraging deep learning techniques. The process begins with acquiring essential images through established benchmarking techniques. These images are then processed in the abnormality segmentation phase, utilizing Mobile-Unet++ (MUnet++) for segmentation. Following segmentation, the images undergo a classification phase, where brain tumors are classified using Multi-scale Mobilenet (MMNet). To validate effectiveness of proposed technique, an efficacy study is conducted, comparing it against traditional methodologies for segmentation and classification of brain.

关键词： Deep learning image segmentation Accuracy Decision making Manuals Medical services Brain modeling Oncology Medical diagnostic imaging Diseases

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