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Advanced Lung Region analysis and Anomaly Detection in CXR images using Optimized VGG-16 Structure

Advanced Lung Region Analysis and Anomaly Detection in CXR I...

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Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA), International conference on

作者： Jose Saji Kumar F Muneeswaran V Robert R Department of Electronics and Communication Engineering Kalasalingam Academy of Research and Education Tamil Nadu India Department of Electronics and Communication Engineering Stella Mary’s College of Engineering Tamil Nadu India

ISBN: (数字)9798331525439

ISBN: (纸本)9798331525446

The development of sophisticated frameworks for Chest X-Ray (CXR) analysis is required due to the growing need for precise and effective indicators in medical imaging. Therefore, this paper proposed a novel VGG-Residual Net architecture for the efficient diagnosis of Tuberculosis (TB). The preprocessing stage employs a Savitzky-Golay (S-G) filter to smooth noisy image data, ensuring high-quality inputs for analysis. Lung region segmentation is performed using the mean shift topology, which clusters pixels based on intensity and spatial proximity, effectively delineating anatomical structures. Local Binary pattern (LBP) is used for feature extraction, capturing critical texture features that characterize spatial patterns in the lung region. The classification process leverages the VGG-Residual Net architecture to detect anomalies with higher accuracy. For the valuation of proposed topology, it is implemented in Python software and the outcomes ensure reliable and accurate diagnostic outcomes, even with complex datasets and limited labeled samples. It achieved good performance in terms of accuracy of 98.90%, precision of 97%, recall of 98%, and specificity of 98% compared to current advanced deep learning models.

关键词： image segmentation Accuracy Tuberculosis Lungs Computer architecture Feature extraction Topology Anomaly detection X-ray imaging Biomedical imaging

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Prediction of SO2 Air Pollution Quality Parameter of Kolhapur City Using Time Series analysis 3rd

Prediction of SO2 Air Pollution Quality Parameter of Kolhapu...

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3rd International conference on Recent Trends in image Processing and pattern recognition, RTIP2R 2020

作者： Muley, Aniket Tangawade, Atish School of Mathematical Sciences SRTM University Nanded Maharashtra India

ISBN: (纸本)9789811605062

This paper deals with the identification of time series predictive model to evaluate the air pollution quality SO2 parameter. In recent years, due to modernization, lots of changes have been done already in the air quality of the environment. A time series model has been developed for SO2 parameter. The performance has been measured through statistical measures. The proposed time series model (2, 0, 1) will be helpful to identify the near future situation of air pollutants may present in the air of Kolhapur city. Further, it will help us to reduce the pollution parameter level and implement some remedies to decrease it. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Quality control

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Automatic Diagnosis of COPD in Lung CT images based on Multi-View DCNN 10th

Automatic Diagnosis of COPD in Lung CT Images based on Multi...

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10th International conference on pattern recognition Applications and Methods (ICPRAM)

作者： Bao, Yin Al Makady, Yasseen Mahmoodi, Sasan Univ Southampton Sch Elect & Comp Sci Univ Rd Southampton Hants England

ISBN: (纸本)9789897584862

Chronic obstructive pulmonary disease (COPD) has long been one of the leading causes of morbidity and mortality worldwide. Numerous studies have shown that CT image analysis is an effective way to diagnose patients with COPD. Automatic diagnosis of CT images using computer vision will shorten the time a patient takes to confirm COPD. This enables patients to receive timely treatment. CT images are three-dimensional data. The extraction of 3D texture features is the core of classification problem. However, the classification accuracy of the current computer vision models is still not high when extracting these features. Therefore, computer vision assisted diagnosis has not been widely used. In this paper, we proposed MV-DCNN, a multi-view deep neural network based on 15 directions. The experimental results show that compared with the state-of-art methods, this method significantly improves the accuracy of COPD classification, with an accuracy of 97.7%. The model proposed here can be used in the medical institutions for diagnosis of COPD.

关键词： COPD Deep Convolutional Neural Network Multi-View Classification

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Segmentation of Large Historical Manuscript Bundles into Multi-page Deeds 11th

Segmentation of Large Historical Manuscript Bundles into M...

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11th iberian conference on pattern recognition and image analysis, IbPRIA 2023

作者： Prieto, Jose Ramón Becerra, David Toselli, Alejandro Hector Alonso, Carlos Vidal, Enrique PRHLT Research Center Universitat Politècnica de València Valencia Spain Valencian Graduate School and Research Network of Artificial Intelligence Valencia Spain tranSkriptorium IA Valencia Spain Universidad de Sevilla Seville Spain

ISBN: (纸本)9783031366154

Archives around the world have vast uncatalogued series of image bundles of digitized historical manuscripts containing, among others, notarial records also known as "deeds" or "acts". One of the first steps to provide metadata which describe the contents of those bundles is to segment these bundles into their individual deeds. Even if deeds are page-aligned, as in the bundles considered in the present work, this is a time-consuming task, often prohibitive given the huge scale of the manuscript series involved. Unlike traditional Layout analysis methods for page-level segmentation, our approach goes beyond the realm of a single-page image, providing consistent deed detection results on full bundles. This is achieved in two tightly integrated steps: first, the probabilities that each bundle image is an "initial", "middle" or "final" page of a deed are estimated, and then an optimal sequence of page labels is computed at the whole bundle level. Empirical results are reported which show that this approach achieves almost perfect segmentation of bundles of a massive Spanish series of historical notarial records. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： image analysis

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Proxemics-Net: Automatic Proxemics recognition in images 11th

Proxemics-Net: Automatic Proxemics Recognition in Images

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11th iberian conference on pattern recognition and image analysis, IbPRIA 2023

作者： Jiménez-Velasco, Isabel Muñoz-Salinas, Rafael Marín-Jiménez, Manuel J. Department Computing and Numerical Analysis University of Córdoba Córdoba Spain Córdoba Spain

ISBN: (纸本)9783031366154

Proxemics is a branch of anthropology that studies how humans use personal space as a means of nonverbal communication;that is, it studies how people interact. Due to the presence of physical contact between people, in the problem of proxemics recognition in images, we have to deal with occlusions and ambiguities, which complicates the process of recognition. Several papers have proposed different methods and models to solve this problem in recent years. Over the last few years, the rapid advancement of powerful Deep Learning techniques has resulted in novel methods and approaches. So, we propose Proxemics-Net, a new model that allows us to study the performance of two state-of-the-art deep learning architectures, ConvNeXt and Visual Transformers (as backbones) on the problem of classifying different types of proxemics on still images. Experiments on the existing Proxemics dataset show that these deep learning models do help favorably in the problem of proxemics recognition since we considerably outperformed the existing state of the art, with the ConvNeXt architecture being the best-performing backbone. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Deep learning

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image Manipulation Detection and Classification Using SIFT and SURF with Support Vector Machine Classifier

Image Manipulation Detection and Classification Using SIFT a...

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Mobile Networks and Wireless Communications (ICMNWC), IEEE International conference on

作者： Ghazi Mohamad Ramadan Zamen Latef Naser Sripelli Jagadish Rekha Phadke Muntather Almusawi College of MLT Ahl Al Bayt University Karbala Iraq National University of Science and Technology Dhi Qar Iraq School of Computer Science & Artificial Intelligence SR University Warangal Telangana India Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India The Islamic University Najaf Iraq

Enhance digital trust and integrity by mastering image forgery detection in machine learning. Uncover the hidden truth behind visuals, ensuring authenticity in a world inundated with digital content. Various image manipulation detection machines have been innovated in the past, but these models face limitations like demanding domain expertise, hyperparameter tuning, time-consuming processes, and overlapping issues. Initially employ the histogram equalization to enhance dataset images, reducing unwanted noise and generating synthetic data for class balance. The pre-processed images are then fed into the model for ongoing analysis. In this paper, propose a Scale-Invariant Feature Transform (SIFT) and Speeded-Up Robust Features (SURF)-based SVM for image manipulation detection and classification, achieving outstanding performance metrics on the CASIA 1.0 dataset: 0.96 accuracy, 0.8120 IOU score, 0.7710 MCC, 0.8850 F1 score, 0.98 precision, and 0.92 sensitivity. This model surpasses the capabilities of existing models, including the Two-Stream Boundary-aware Network, Discrete Cosine Transformation (DCT)-based multitask learning network, and Multilevel Local Binary pattern Texture Descriptor.

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An Identification Method of image-based for Output pattern of Metamorphic Relation in Burnup Calculation Program 3

An Identification Method of Image-based for Output Pattern o...

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3rd International Academic Exchange conference on Science and Technology Innovation, IAECST 2021

作者： Fu, Shengfu Yang, Xiaohua Li, Meng Wang, Fan Eng. and Technol. Res. Ctr. of Software Eval. and Test. for Intellectual Equipment of Hunan Province Hunan Prov. Hengyang421001 China CNNC Key Laboratory on High Trusted Computing Hunan Prov. Hengyang421001 China School of Computer Science and Technology University of South China Hunan Hengyang421001 China

ISBN: (纸本)9781665402675

It is challenging to verify the design and analysis software adequately for nuclear power areas because of the inflated cost, time-consuming benchmark problems development, and few verification examples. This situation is called the test oracle problem. Metamorphic testing cleverly combines the specific properties of physical equations and numerical algorithms with software verification. Without constructing numerical solutions or verification examples, it verifies the software by checking whether the input and output of multiple executions meet the metamorphic relations. Since metamorphic relation is the nature rules of physical models and numerical algorithms, it is not restricted to specific code implementation technology. Therefore, it is no doubt that metamorphic testing is a promising technique to alleviate the test oracle *** relation is the key to metamorphic testing, which consists of input pattern and output pattern. The former can be obtained through manual analysis by industry experts, while the latter does not have a system identification method. An output pattern identification method based on the solution figure has been introduced here. Specifically, the input pattern is received by artificially analyzing the physical property of the program under test. Next, a group of test inputs has been generated according to such patterns. The calculation results are received after those inputs drive the program. Moreover, the function form of the output pattern is guided by the image of the results. Finally, metamorphic relation is constructed by input pattern and output pattern. For a demonstration of this method's details, a nuclide burnup calculation code is employed here, namely NUIT. By investigating the resulting image of 3820 isotopes, their output pattern classification models have been established according to the burnup depth and nuclide density. It includes four categories and seventeen sub-categories. The verification of other burnup

关键词： Verification

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Face Morphing Attack Detection in the Presence of Post-processed image Sources Using Neighborhood Component analysis and Decision Tree Classifier 3rd

Face Morphing Attack Detection in the Presence of Post-proce...

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3rd International conference on Information and Communication Technology and Applications, ICTA 2020

作者： Kenneth, Ogbuka Mary Bashir, Sulaimon Adebayo Abisoye, Opeyemi Aderiike Mohammed, Abdulmalik Danlami Department of Computer Science Federal University of Technology Minna Nigeria

ISBN: (纸本)9783030691424

Recently, Face Morphing Attack Detection (MAD) has gained a great deal of attention as criminals have started to use freely and easily available digital manipulation techniques to combine two or more subject facial images to create a new facial image that can be viewed as an accurate image of any of the individual images that constitute it. Some of these morphing tools create morphed images of high quality which pose a serious threat to existing Face recognition Systems (FRS). In the literatures, it has been identified that FRS is vulnerable to multiform morphing attacks. Based on this vulnerability, several types of research on the detection of this morph attack was conducted using several techniques. Despite the remarkable levels of MAD reported in various literature, so far no suitable solution has been found to handle post-processed images such as images modified after morphing with sharpening operation that can dramatically reduce visible artifacts of morphed photos. In this work, an approach is proposed for MAD before image post-processing and after image post-processing built on a combination of Local Binary pattern (LBP) for extraction of feature, Neighborhood Component analysis (NCA) for selection of features and classification using K-Nearest Neighbor (KNN), Decision Tree Classifier (DTC) and Naïve Bayes (NB) classifier. The outcome gotten by training the different classifiers with feature vectors selected using the NCA algorithm improved the classification accuracy from 90% to 94%, consequently improving the general performance of the MAD. © 2020, Springer Nature Switzerland AG.

关键词： Local binary pattern

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Design and Research on Visual recognition System for Fruit Sorting Collaborative Robot

Design and Research on Visual Recognition System for Fruit S...

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Networking Systems of AI (INSAI), International conference on

作者： Yingchen Ma Hanan Zhu Hongli Jia Chunlai Guo Jiaqi Li Zhe Li College of Intelligent Engineering Harbin Institute of Petroleum Harbin China College of Intelligent Engineering Heilongjiang Agricultural Engineering Vocational College Harbin China IE Technology Research Center Harbin Cambridge University Harbin China

ISBN: (数字)9798350344660

ISBN: (纸本)9798350344677

This study aimed to enhance the efficiency and accuracy of fruit sorting through the development of a collaborative robotic vision recognition system based on neural networks. Leveraging convolutional neural networks (CNNs), a substantial dataset of fruit images was collected, preprocessed, and utilized for training. The core algorithm employed was the neural network, particularly effective in pattern recognition and feature extraction. Integration with the control system of collaborative robots allowed real-time fruit identification and analysis using visual sensors during sorting. The system's design considered optimization and acceleration of neural networks, incorporating advanced deep learning techniques like CNNs and recurrent neural networks. This fusion of collaborative robots, neural networks, and visual recognition technologies significantly improved fruit sorting efficiency and accuracy. The automated sorting process achieved through thoughtful system design and optimization reduces labor costs and enhances productivity. Despite notable progress in the design and research of collaborative robotic and neural network-based vision recognition systems, challenges and areas for improvement persist. Future work may involve further optimizing neural network models for increased precision and robustness and refining collaborative robot control strategies for more complex task collaboration. The collaborative robotic and neural network-based vision recognition system opens new possibilities in the field of fruit sorting, propelling the industry towards intelligent and efficient practices, thereby creating greater economic and societal value.

关键词： Visualization Accuracy image recognition Service robots Machine vision Neural networks Collaborative robots Real-time systems Convolutional neural networks Sorting

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Keywords recognition from EEG Signals on Smart Devices a Novel Approach 3rd

Keywords Recognition from EEG Signals on Smart Devices a Nov...

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3rd International conference on Recent Trends in image Processing and pattern recognition, RTIP2R 2020

作者： Bedre, Sushil Pandharinath Jha, Subodh Kumar Borde, Prashant Patil, Chandrakant Gawali, Bharati Yannawar, Pravin Vision and Intelligent System Laboratory Dr. Babasaheb Ambedkar University Aurangabad Maharashtra431004 India SCMRL Lab Dr. Babasaheb Ambedkar University Aurangabad Maharashtra431004 India

ISBN: (纸本)9789811604928

The Advancement of communication system has given us the freedom to think beyond traditional communication system and stage is set for thought oriented communication system. There are thousands of thoughts generated and vanished in a timeframe but out of these some prominent thoughts persist and we proceed with the same in our day to day activities. The advancement in Electroencephalogram has provided a chance to see the activity in the human brain in non-invasive manner. The proposed research work presents the method for Digit recognition using the EEG signals acquired and processed on smart devices. The results show the implementation of Computation neural network for the recognition of digits from EEG signals. It was seen that, the 90.64% correct classification was achieved. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Independent component analysis

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