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A comprehensive survey on convolutional neural network in medical image analysis

MULTIMEDIA TOOLS AND applications 2022年第29期81卷 41361-41405页

作者： Yao, Xujing Wang, Xinyue Wang, Shui-Hua Zhang, Yu-Dong Univ Leicester Sch Informat Leicester LE1 7RH Leics England King Abdulaziz Univ Fac Comp & Informat Technol Dept Informat Syst Jeddah 21589 Saudi Arabia Loughborough Univ Sch Architecture Bldg & Civil Engn Loughborough LE11 3TU Leics England

CNN is inspired from Primary Visual (V1) neurons. It is a typical deep learning technique and can help teach machine how to see and identify objects. In the most recent decade, deep learning develops rapidly and has been well used in various fields of expertise such as computer vision and natural language processing. As the representative algorithm of deep learning, Convolution Neural Network (CNN) has been regarded as a breakthrough of historic significance in image processing and visual recognition tasks since the astonishing results achieved on imageNet Large Scale Visual Recognition Competition (ILSVRC) Unlike methods based on handcrafted features, CNN models can build high-level features from low-level ones in a data-driven fashion and have displayed great potential in medical image analysis among the aspects of segmentation of histological images identification, lesion detection, tissue classification, etc. This paper provides a review on CNN from the perspectives of its basic mechanism introduction, structure, typical architecture and main application in medical image analysis through analyzing over 100 references from Google Scholar, PubMed, Web of Science and various sources published from 1958 to 2020.

关键词： Deep learning Feedforward Neural Network Convolutional neural network Breast Cancer Lung Nodule Brain Tumor Medical image analysis

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Revolutionizing machine vision: Advanced Convolutional Strategies for Rapid image processing

Revolutionizing Machine Vision: Advanced Convolutional Strat...

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Information and Communication Technology (ICTech), International Conference of

作者： Hanlei Wu Pittsburgh Institute Sichuan University Chengdu China

ISBN: (数字)9798350376258

ISBN: (纸本)9798350376265

This paper presents a comprehensive examination of innovative strategies aimed at enhancing machine vision technology, particularly in the context of energy efficiency and processing speed, critical factors for applications like facial recognition. The study focuses on three distinct approaches: an optimized two-dimensional convolution algorithm, a novel Field-Programmable Gate Array (FPGA) implementation, and advancements in multichannel meta-imagers. Firstly, the paper discusses an optimized algorithm for two-dimensional convolutions, a fundamental operation in machine vision. This advanced algorithm significantly reduces computational complexity. For instance, in executing a two-dimensional 3×3 cyclic convolution, the proposed method reduces the number of necessary multiplications from 81 to merely 13, offering a substantial improvement in efficiency. Secondly, the paper explores an innovative FPGA implementation of the two-dimensional convolution algorithm. This implementation is designed to minimize the use of shift registers, multipliers, and adders. As a result, it utilizes fewer Look-Up Tables (LUTs), leading to energy and time savings in executing the convolution process. The paper details the architecture of this FPGA-based approach and its implications for energy consumption and processing speed in machine vision applications. Finally, the paper introduces a novel technique called the Avg-Topk method, addressing a critical challenge in the pooling layer of convolutional neural networks. This method combines the benefits of average pooling with the advantages of max pooling, aiming to enhance the accuracy of the pooling layer without compromising on efficiency. The Avg-Topk method represents a significant step forward in optimizing the pooling process within machine vision systems. In summary, this paper delves into groundbreaking methods to improve the speed and energy efficiency of machine vision systems, offering valuable insights and potential solution

关键词： image resolution Accuracy Convolution machine vision Shift registers Energy efficiency Table lookup Classification algorithms Usability Field programmable gate arrays

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A Hybrid Density-Based Clustering Pipeline for Track Reconstruction

A Hybrid Density-Based Clustering Pipeline for Track Reconst...

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image processing, Computer vision and machine Learning (ICICML), International Conference on

作者： Bijia You Zhiyun Xia School of Computer Science Beijing University of Posts and Telecommunications Beijing China School of Cyberspace Security Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350355413

ISBN: (纸本)9798350355420

In high-energy physics, the capability to accurately and efficiently track charged particles is essential for effective data analysis. This article introduces an innovative density-based clustering pipeline intended for the track reconstruction task, incorporating Density-Based Spatial Clustering of applications with Noise (DBSCAN) algorithm and Ordering Points To Identify the Clustering Structure (OPTICS) algorithm. Results on simulated data suggest that the proposed method offers improvements in both effectiveness and robustness compared to traditional techniques, with performance on par with state-of-the-art neural network-based approaches. Furthermore, this pipeline demonstrates significant potential for real-time applications in high-energy physics experiments, offering a scalable and robust solution.

关键词： machine learning algorithms Pipelines Noise Clustering algorithms Optics Robustness Real-time systems Pattern recognition Trajectory image reconstruction

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Target tracking using video surveillance for enabling machine vision services at the edge of marine transportation systems based on microwave remote sensing

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JOURNAL OF CLOUD COMPUTING-ADVANCES SYSTEMS AND applications 2024年第1期13卷 47-47页

作者： Li, Meiyan Wang, Qinyong Liao, Yuwei Baise Univ Sch Informat Engn Baise 533000 Peoples R China Zhejiang Coll Secur Technol Coll Artificial Intelligence Wenzhou 325000 Peoples R China Guangxi Baise Agr Sch Ind Robot Technol Baise 533000 Peoples R China

Automatic target tracking in emerging remote sensing video-generating tools based on microwave imaging technology and radars has been investigated in this paper. A moving target tracking system is proposed to be low complexity and fast for implementation through edge nodes in a mini-satellite or drone network enabling machine intelligence into large-scale vision systems, in particular, for marine transportation systems. The system uses a group of image processing tools for video pre-processing, and Kalman filtering to do the main task. For testing the system performance, two measures of accuracy and false alarms probability are computed for real vision data. Two types of scenes are analyzed including the scene with single target, and the scene with multiple targets that is more complicated for automatic target detection and tracking systems. The proposed system has achieved a high performance in our tests.

关键词： Edge computing Radar imaging Microwave remote sensing Automatic target tracking False alarm

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Asynchronous Perception machine for Efficient Test Time Training 38

Asynchronous Perception Machine for Efficient Test Time Trai...

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38th Conference on Neural Information processing Systems, NeurIPS 2024

作者： Modi, Rajat Singh Rawat, Yogesh Centre for Research in Computer Vision University of Central Florida OrlandoFL32765 United States

In this work, we propose Asynchronous Perception machine (APM), a computationally-efficient architecture for test-time-training (TTT). APM can process patches of an image one at a time in any order asymmetrically, and still encode semantic-awareness in the net. We demonstrate APM's ability to recognize out-of-distribution images without dataset-specific pre-training, augmentation or any-pretext task. APM offers competitive performance over existing TTT approaches. To perform TTT, APM just distills test sample's representation once. APM possesses a unique property: it can learn using just this single representation and starts predicting semantically-aware features. APM demostrates potential applications beyond test-time-training: APM can scale up to a dataset of 2D images and yield semantic-clusterings in a single forward pass. APM also provides first empirical evidence towards validating GLOM's insight, i.e. if input percept is a field. Therefore, APM helps us converge towards an implementation which can do both interpolation and perception on a shared-connectionist hardware. Our code is publicly available at this link. © 2024 Neural information processing systems foundation. All rights reserved.

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Illumination Consistency processing Based on Illumination Domain Signal-Guided Unsupervised Generative Adversarial Network for Flotation Froth images

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Wang, Xiaoli Zhang, Yinan Kong, Lingshuang Zhou, Jiayi Yang, Chunhua Cent South Univ Sch Automat Changsha 410083 Peoples R China Changsha Univ Sch Elect Informat & Elect Engn Changsha 410083 Peoples R China

In the machine vision-based online monitoring of the flotation process, froth images acquired in real-time are subject to color distortion and excessive bright spots caused by inconsistent illumination, which hinders the effectiveness of image analysis and further online measurement for operating performance indicators. Current image processing methods struggle to correct color distortion and remove excess bright spots in froth images simultaneously. Therefore, in this article, an illumination domain signal-guided unsupervised generative adversarial network (IDS-GUGAN) is proposed for illumination consistency processing of flotation froth images. First, considering the varying effects of inconsistent illumination on froth images, the illumination domain signal-guided image generation (IDS-GIG) mechanism based on the theory of unsupervised disentangled representation learning is designed to achieve adaptive correction of froth images with varying degrees of distortion. Moreover, a novel lightweight double-closed-loop network architecture is introduced to support unsupervised learning utilizing unpaired froth images and improve computational efficiency, which makes the proposed approach highly suitable for industrial applications. Comprehensive experiments on a real tungsten cleaner flotation process dataset and two public benchmark datasets related to image illumination processing tasks consistently endorse the superiority of IDS-GUGAN.

关键词： Flotation froth image generative adversarial network (GAN) illumination consistency processing unsupervised disentangled representation learning Flotation froth image generative adversarial network (GAN) illumination consistency processing unsupervised disentangled representation learning

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Improving Myanmar image Caption Generation Using NASNetLarge and Bi-directional LSTM

Improving Myanmar Image Caption Generation Using NASNetLarge...

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2023 IEEE Conference on Computer applications, ICCA 2023

作者： Aung, San Pa Pa Pa, Win Pa Nwe, Tin Lay University of Computer Studies Natural Language Processing Lab Yangon Yangon Myanmar Institute for Infocomm Research Visual Intelligence Department Singapore Singapore

ISBN: (纸本)9781665435994

The main objective of this paper is to improve the automatic Myanmar captions by learning the contents of images using NASNetLarge and Bi-LSTM model. Describing the contents of an image is a complex task for machine without human intervention. Computer vision and Natural Language processing are widely used to tackle this problem. This paper proposed a deep learning-based Myanmar image captioning system which used a NASNetLarge feature extraction model of CNN as an encoder and a deep Recurrent Neural Network (RNN) with Bi-directional Long Short-Term Memory (LSTM) as a decoder. For corpus construction, we created and annotated the Myanmar image captions corpus (consists of over 40k Myanmar sentences), which is based on Flickr8k dataset. Furthermore, two different types of segmentations such as word segmentation level and syllable segmentation level are studied in text preprocessing step. In this work, the proposed Bi-directional LSTM model is compared with LSTM, GRU as well as the baseline model. Experiments on the updated dataset is presented that all of our models using syllable segmentation give higher and comparable BLEU scores than word segmentation for Myanmar image captioning system. NASNetLarge with Bi-directional LSTM model using syllable segmentation approach achieved the highest BLEU-4 score 40.05% which is 12.5% better than word segmentation in this work and 15.67% BLEU-4 score better than our previous work. © 2023 IEEE.

关键词： Long short-term memory

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machine learning ensemble with image processing for pest identification and classification in field crops

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NEURAL COMPUTING & applications 2021年第13期33卷 7491-7504页

作者： Kasinathan, Thenmozhi Uyyala, Srinivasulu Reddy Natl Inst Technol Tiruchirappalli Dept Comp Applicat Ctr Excellence Artificial Intelligence Machine Learning & Data Analyt Lab Tiruchirappalli 620015 Tamil Nadu India

In agriculture field, yield loss is a major problem due to attack of various insects in field crops. Traditional insect identification and classification methods are time-consuming and require entomologist experts. Early information about the attack of insects helps farmers to control the crop damage to improve the productivity and reduce the use of pesticides. This research work focuses on the classification of crop insects by applying machine vision and knowledge-based techniques with image processing by using different feature descriptors including texture, color, shape, histogram of oriented gradients (HOG) and global image descriptor (GIST). A combination of all these features was used in the classification of insects. In this research, several machine learning algorithms including both base classifiers and ensemble classifiers were applied for three different insect datasets and the performances of classification results were evaluated by majority voting. Naive bayes (NB), support vector machine (SVM), K-nearest-neighbor (KNN) and multi-layer perceptron (MLP) were used as base classifiers. Ensemble classifiers include random forest (RF), bagging and XGBoost were utilized;10-fold cross-validation test was conducted to achieve a better classification and identification of insects. The experimental results showed that the classification accuracy is improved by majority voting with ensemble classifiers in the combination of texture, color, shape, HOG and GIST features.

关键词： Crops Ensemble classification image processing Insect classification machine learning algorithm Majority voting

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machine learning algorithm for Avocado image segmentation based on quantum enhancement and Random forest 2

Machine learning algorithm for Avocado image segmentation ba...

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2nd International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET)

作者： El Amraoui, Khalid Ezzaki, Ayoub Masmoudi, Lhoussaine Hadri, Majid El Belrhiti, Hicham El Ansari, Mohamed Amari, Aziz Mohammed 5 Univ Rabat LCS Lab Phys Dept Fac Sci Rabat Morocco Mohammed 5 Univ Rabat Allied Fundamental Sci Dept Agron & Vet Inst Hassan II Rabat Morocco Moulay Ismail Univ Informt & Applicat Lab Fac Sci Meknes Morocco

ISBN: (纸本)9781665422093

Precision agriculture (PA) represents the use of new technologies, specially computer vision, to increase agricultural productivity, where image segmentation plays a crucial role in several PA applications. This paper presents a machine learning algorithm for Avocado image segmentation based on quantum enhancement and Random forest. In order to show the performance of the proposed method in term of segmentation, which represents one of the most sensible computer vision technics to noise and low illumination images, a set of experimentations based on synthetic and real images devoted to agricultural applications (avocado fruit detection and localization) are done. The Segmentation accuracy (SA) and the mean intersection over Union (MIoU) metrics are adopted to evaluate its performance against other algorithms presented in the literature. The proposed method shows good results in terms of segmentation quality, sensibility to noise and low illumination conditions, outperforming the existing and widely used binarization methods.

关键词： Segmentation Quantum mechanics machine learning precision agriculture

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Experimental multi-scale characterization of mode-ii interlaminar fracture in geometrically scaled stitched and unstitched resin-infused composites

Experimental multi-scale characterization of mode-II interla...

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AIAA SciTech Forum

作者： Ozborn, Dawson Black, Jackob Huberty, Wayne Bounds, Christopher Kim, Han-Gyu Mississippi State Univ Dept Aerosp Engn Mississippi State MS 39762 USA Mississippi State Univ Adv Composites Inst Mississippi State MS 39762 USA

ISBN: (数字)9781624107115

ISBN: (纸本)9781624107115

This work is focused on investigating the impact of out-of-plane stitches on enhancing mode-ii interlaminar fracture toughness (or energy) and characterizing damage progression and crack arrestment in stitched resin-infused composites. For the experimental work, End-Notched Flexure (ENF) quasi-isotropic specimens were manufactured using +/- 45 non-crimp carbon-fiber fabrics through a resin-infusion process. Both stitched and unstitched specimen sets were designed for comparison. For a size effect study, the ENF specimens were geometrically scaled with three scaling levels. Based on the load-displacement data (i.e., global analysis), the fracture energy of the specimen material was analyzed using the compliance calibration method and a size effect theory. The fracture energy values were compared between the stitched and unstitched cases to characterize the enhanced fracture toughness of stitched composites. For local analysis, two types of digital image correlation (DIC) systems were employed: microscopic and macroscopic (i.e., coupon-scale) DIC systems. By analyzing in-plane displacement through the thickness, separation development was characterized along predicted fracture process zones. The impact of out-of-plane stitches on separation propagation along fracture process zones was discussed based on the DIC analysis. This work will contribute to developing a high-fidelity damage model for stitched resin-infused composites in the form of a traction-separation for high-speed aircraft applications.

关键词： Fracture Toughness Cohesive Zone Model Resin Transfer Molding Regression Analysis Aerothermodynamics Composite Aircraft Composite Materials Scaled Composites Aircraft Design machine vision

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