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Paradigm shift from artificial neural networks (ANNs) to deep Convolutional neural networks (DCNNs) in the field of medical image processing

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EXPERT SYSTEMS WITH applications 2024年 244卷

作者： Abut, Serdar Okut, Hayrettin Kallail, K. James Siirt Univ Dept Comp Engn TR-56100 Siirt Turkiye Univ Kansas Dept Off Res Sch Med 1010 N Kansas Wichita KS 67214 USA Univ Kansas Dept Populat Hlth Sch Med 1010 N Kansas Wichita KS 67214 USA Univ Kansas Dept Internal Med Sch Med 1010 N Kansas Wichita KS 67214 USA

images and other types of unstructural data in the medical domain are rapidly becoming data-intensive. Actionable insights from these complex data present new opportunities but also pose new challenges for classification or segmentation of unstructural data sources. Over the years, medical problems have been solved by combining traditional statistical methods with image processing methods. Both the increase in the size of the data and the increase in the resolution are among the factors that shape the ongoing improvements in artificial intelligence (AI), particularly concerning deep learning (DL) techniques for evaluation of these medical data to identify, classify, and quantify patterns for clinical needs. At this point, it is important to understand how artificial neural networks (ANNs), which are an important milestone in interpreting big data, transform into Deep Convolutional neural networks (DCNNs) and to predict where the change will go. We aimed to explain the needs of these stages in medical image processing through the studies in the literature. At the same time, information is provided about the studies that lead to paradigm shift and try to solve the image related medical problems by using DCNNs. With the increase in the knowledge of medical doctors on this subject, it will be possible to look at the solution of new problems in computer science from different perspectives.

关键词： Deep Convolutional neural networks Medical image processing artificial neural networks Feature Extraction

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Quaternion neural networks: A physics-incorporated intelligence framework

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IEEE SIGNAL processing MAGAZINE 2024年第3期41卷 88-100页

作者： Hirose, Akira Shang, Fang Otsuka, Yuta Natsuaki, Ryo Matsumoto, Yuya Usami, Naoto Song, Yicheng Chen, Haotian Univ Tokyo Dept Elect Engn & Informat Syst Tokyo 1138656 Japan Univ Electrocommun Grad Sch Informat & Engn Tokyo 1828585 Japan NEC Corp Ltd Visual Intelligence Res Labs Kawasaki 2118666 Japan Japan Aerosp Explorat Agcy JAXA Inst Space & Astronaut Sci Sagamihara 2525210 Japan Univ Tokyo Dept Elect Engn & Informat Syst Tokyo Japan

Why quaternions in neural networks (NNs)? Are there quaternions in the human brain? "No" may be an ordinary answer. However, quaternion NNs (QNNs) are a powerful framework that strongly connects artificial intelligence (AI) and the real world. In this article, we deal with NNs based on quaternions and describe their basics and features. We also detail the underlying ideas in their engineering applications, especially when we adaptively process the polarization information of electromagnetic waves. We focus on their role in remote sensing, such as Earth observation radar mounted on artificial satellites or aircraft and underground radar, as well as mobile communication. There, QNNs are a class of NNs that know physics, especially polarization, composing a framework by fusing measurement physics with adaptive-processing mathematics. This fusion realizes a seamless integration of measurement and intelligence, contributing to the construction of a human society having harmony between AI and real human lives.

关键词： Radar remote sensing Quaternions image processing Airborne radar artificial neural networks Mobile communication artificial intelligence neural networks Physics Hypercomplex

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image and video analysis using graph neural network for Internet of Medical Things and computer vision applications

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CAAI TRANSACTIONS ON INTELLIGENCE TECHNOLOGY 2024年第0期

作者： Sharma, Amit Sharma, Ashutosh Tselykh, Alexey Bozhenyuk, Alexander Kim, Byung-Gyu Southern Fed Univ Inst Comp Technol & Informat Secur Taganrog Russia Chitkara Univ Inst Engn & Technol Rajpura Punjab India Henan Univ Sci & Technol Business Sch Luoyang Henan Peoples R China Southern Fed Univ Dept Informat & Analyt Secur Syst Rostov Na Donu Russia Sookmyung Womens Univ Div Artificial Intelligence Engn Seoul South Korea

Graph neural networks (GNNs) have revolutionised the processing of information by facilitating the transmission of messages between graph nodes. Graph neural networks operate on graph-structured data, which makes them suitable for a wide variety of computer vision problems, such as link prediction, node classification, and graph classification. The authors explore in depth the applications of GNNs in computer vision, including their design considerations, architectural challenges, applications, and implementation concerns. While conventional convolutional neural networks (CNNs) excel at object recognition in images and videos, GNN architectures offer a novel method for addressing various image and video comprehension challenges. A novel deep neural network-based model for image and video analysis is proposed, which combines a neural network with fully connected layers on a graph. The proposed architecture extracts highly discriminative information from images and videos by leveraging the graph structure. Also, the investigation focuses on the enhancement of underlying connection network estimation using cutting-edge graph learning algorithms. Experimental results on real-world datasets demonstrate that the proposed GNN model is preferable to existing state-of-the-art methods. It obtains a remarkable 96.63% accuracy on the imageNet dataset, outperforming heuristic approaches, artificial neural networks, and conventional CNN techniques. From the results, we can see that GNNs are a potent instrument for graph data analysis and pave the way for machines to achieve human-level visual intuition.

关键词： computer vision deep learning graph theory image analysis Internet of things video signal processing

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Improve accuracy in CNNs while using approximate computing methods

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JOURNAL OF SUPERCOMPUTING 2025年第2期81卷 1-23页

作者： Rafieinejad, Mohammadreza Marvasti, Mohammadreza Binesh Asghari, Seyyed Amir Shahbakhti, Kimiya Kharazmi Univ Dept Elect & Comp Engn Tehran Iran

artificial neural networks have been one of the science's most influential and essential branches in the past decades. neural networks have found applications in various fields including medical and pharmaceutical services, voice and speech recognition, computer vision, natural language processing, and video and image processing. neural networks have many layers and consume much energy. Approximate computing is a promising way to reduce energy consumption in applications that can tolerate a degree of accuracy reduction. This paper proposes an effective method to prevent accuracy reduction after using approximate computing methods in the CNNs. The method exploits the k-means clustering algorithm to label pixels in the first convolutional layer. Then, using one of the existing pruning methods, different pruning amounts have been applied to all layers. The experimental results on three CNNs and four different datasets show that the accuracy of the proposed method has significantly improved (by 17%) compared to the baseline network.

关键词： Approximate computing Computer vision Convolutional neural network Energy consumption Machine learning neural network

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Foundation models in pathology

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PATHOLOGIE 2025年第3期46卷 152-155页

作者： Klauschen, Frederick Dippel, Jonas Mueller, Klaus-Robert Ludwig Maximilians Univ Munchen Pathol Inst Thalkirchner Str 36 D-80337 Munich Germany Berlin Inst Fdn Learning & Data BIFOLD Berlin Germany Bayer Zentrum Krebsforschung BZKF Munich Germany Tech Univ Berlin Machine Learning Grp Berlin Germany Aignostics GmbH Berlin Germany Korea Univ Dept Artificial Intelligence Seoul South Korea Max Planck Inst Informat Saarbrucken Germany

Foundation models prepare neural networks for applications in specific domains, such as speech applications or image analysis, through self-supervised pretraining. These models can be adapted for specific applications, such as histopathological diagnostics. While adaptation still requires supervised training, AI applications based on foundation models achieve significantly better prediction accuracy with fewer training data compared to conventional approaches. This article introduces the topic and provides an overview of foundation models in pathology.

关键词： artificial intelligence Computer-assisted image processing Histopathology Machine learning Machine learning Foundation models Foundation models neural networks neural networks Machine learning Foundation models neural networks

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Balancing precision and efficiency: an approximate multiplier with built-in error compensation for error-resilient applications

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JOURNAL OF SUPERCOMPUTING 2025年第1期81卷 1-17页

作者： Sayadi, Ladan Amirany, Abdolah Moaiyeri, Mohammad Hossein Timarchi, Somayeh Shahid Beheshti Univ Fac Elect Engn Tehran Iran George Washington Univ Dept Elect & Comp Engn Washington DC USA Queen Mary Univ London Sch Elect Engn & Comp Sci London E1 4NS England

In the pursuit of high-performance designs for error-resilient applications, approximate computing emerges as a key strategy. This paper introduces an innovative approximate multiplier, leveraging two highly efficient compressors. These compressors operate in tandem across two stages, strategically compensating for errors and culminating in a multiplier that maintains accuracy and significantly reduces delay in the final stage. The proposed method is specifically tailored for applications reliant on multiplication, such as image processing and neural networks. HSPICE simulations were conducted using 7 nm FinFET technology to gauge its efficacy. Results indicate a remarkable 82% reduction in power-delay product (PDP) compared to traditional multipliers. Moreover, system-level simulations underscore the practicality of the proposed multiplier in real-world applications like image processing and artificial intelligence, revealing minimal compromise in accuracy. This work contributes a nuanced perspective to approximate computing, presenting a multiplier poised to elevate efficiency without sacrificing precision in critical domains.

关键词： Approximate multiplier Approximate compressor Low-power design image processing neural networks

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Fractal neural Network Approach for Analyzing Satellite images

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APPLIED artificial INTELLIGENCE 2025年第1期39卷

作者： Shymanskyi, Volodymyr Ratinskiy, Oleh Shakhovska, Nataliya Lviv Polytech Natl Univ Dept Artificial Intelligence Kn Romana Str 5 UA-79905 Lvov Ukraine

Satellites play a critical role in modern technology by providing images for various applications, such as detecting infrastructure and assessing environmental impacts. The author's work investigates the application of Fractal neural networks (FractalNet) for automating the detection of specific objects in satellite images. The study aims to improve processing speed and accuracy compared to traditional Convolutional neural networks (CNNs). The research involves developing and comparing FractalNet with CNNs, focusing on their effectiveness in image classification. The architecture of FractalNet, characterized by recursive structures and deep layers, is evaluated against CNNs like VGG16 and ResNet50. Data collection included manually gathering high-resolution satellite images of specific objects from Google Earth. The neural network models were trained and tested with varying hyperparameters, including learning rates and batch sizes. FractalNet demonstrated superior performance over CNNs, particularly in deep network configurations. The results improved significantly with data augmentation and optimized hyperparameters, achieving a test accuracy of up to 93.26% with a 32-layer model. Fractal neural networks offer a promising approach for automating satellite image analysis, providing better accuracy and robustness compared to traditional CNNs architectures.

关键词： Satellite imagery

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Implementation of Kolmogorov-Arnold networks for Efficient image processing in Resource-Constrained Internet of Things Devices

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TECHNOLOGIES 2025年第4期13卷 155-155页

作者： Shaushenova, Anargul Kuznetsov, Oleksandr Nurpeisova, Ardak Ongarbayeva, Maral S Seifullin Kazakh Agro Tech Res Univ Fac Comp Syst & Profess Educ Dept Informat Syst Astana 010000 Kazakhstan eCampus Univ CO Dept Theoret & Appl Sci Via Isimbardi 10 I-22060 Novedrate CO Italy Kharkov Natl Univ Sch Comp Sci & Artificial Intelligence Dept Intelligent Software Syst & Technol 4 Svobody Sq UA-61022 Kharkiv Ukraine Int Taraz Univ Fac Nat Sci Dept Informat & Commun Technol Taraz 080000 Kazakhstan

This research investigates the implementation of Kolmogorov-Arnold networks (KANs) for image processing in resource-constrained IoTs devices. KANs represent a novel neural network architecture that offers significant advantages over traditional deep learning approaches, particularly in applications where computational resources are limited. Our study demonstrates the efficiency of KAN-based solutions for image analysis tasks in IoTs environments, providing comparative performance metrics against conventional convolutional neural networks. The experimental results indicate substantial improvements in processing speed and memory utilization while maintaining competitive accuracy. This work contributes to the advancement of AI-driven IoTs applications by proposing optimized KAN-based implementations suitable for edge computing scenarios. The findings have important implications for IoTs deployment in smart infrastructure, environmental monitoring, and industrial automation where efficient image processing is critical.

关键词： Kolmogorov-Arnold networks person detection visual wake words lightweight neural networks TinyML resource-constrained computing computer vision efficient inference hybrid neural architectures

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Design Of Convolution neural Network For Crack Detection 7

Design Of Convolution Neural Network For Crack Detection

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7th International conference on Devices, Circuits and Systems (ICDCS)

作者： Malathi, D. Gautham, S. Dineshkumar, M. Balakrishnan, K. Kongu Engn Coll Dept Elect & Commun Engn Erode India

ISBN: (纸本)9798350350470;9798350350487

Crack is an important factor to consider when assessing the quality of concrete structures since it impacts the structure's longevity, application, and safety. Convolutional neural networks are increasingly the best option to replace manual crack detection because of the advancement of methods for deep learning. Machine learning algorithms known as artificial neural networks (ANNs) imitate how the human brain functions. These neural networks can be implemented in software. However, these neural networks require large computations. Hardware implementation of these neural networks has higher processing speeds than their software implementations. CNN is a particular kind of artificial neural network that is used to interpret pixel data and is utilised in image detection and processing. Computer Vision applications including object identification, image segmentation, and image classification work well with convolutional neural networks. employed for categorization The proposed method uses a configurable convolution neural network system for crack detection. An accuracy of 97.5% is achieved over 200 images. By detecting the crack effectively using the method, the quality of the concrete structures will be ensured using dedicated hardware shortly.

关键词： crack detection deep learning convolution neural network image segmentation CNN system image processing image classification

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Generative artificial Intelligence for Hyperspectral Sensor Data: A Review

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2025年 18卷 6422-6439页

作者： Abuhani, Diaa Addeen Zualkernan, Imran Aldamani, Raghad Alshafai, Mohamed Amer Univ Sharjah Dept Comp Sci & Engn Sharjah 26666 U Arab Emirates

Airborne platforms and satellites provide rich sensor data in the form of hyperspectral images (HSI), which are crucial for numerous vision-related tasks, such as feature extraction, image enhancement, and data synthesis. This article reviews the contextual importance and applications of generative artificial intelligence (GAI) in the advancement of HSI processing. GAI methods address the inherent challenges of HSI data, such as high dimensionality, noise, and the need to preserve spectral-spatial correlations, rendering them indispensable for modern HSI analysis. Generative neural networks, including generative adversarial networks and denoising diffusion probabilistic models, are highlighted for their superior performance in classification, segmentation, and object identification tasks, often surpassing traditional approaches, such as U-Nets, autoencoders, and deep convolutional neural networks. Diffusion models showed competitive performance in tasks, such as feature extraction and image resolution enhancement, particularly in terms of inference time and computational cost. Transformer architectures combined with attention mechanisms further improved the accuracy of generative methods, particularly for preserving spectral and spatial information in tasks, such as image translation, data augmentation, and data synthesis. Despite these advancements, challenges remain, particularly in developing computationally efficient models for super-resolution and data synthesis. In addition, novel evaluation metrics tailored to the complex nature of HSI data are needed. This review underscores the potential of GAI in addressing these challenges while presenting its current strengths, limitations, and future research directions.

关键词： Feature extraction Hyperspectral imaging image segmentation Satellites Spatial resolution Machine learning neural networks Reviews Noise measurement Data models Diffusion models generative adversarial networks (GANs) generative artificial intelligence (GAI) generative neural networks (GNNs) hyperspectral images

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