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Unsupervised PulseNet: Automated Pruning of Convolutional neural networks by K-Means Clustering 1

7th International Conference on Machine Learning, Optimization, and Data Science (LOD) / 1st Symposium on artificial Intelligence and Neuroscience (ACAIN)

作者： Browne, David Giering, Michael Prestwich, Steven Univ Coll Cork Cork Ireland United Technol Res Ctr Penrose Quay Cork Ireland

ISBN: (数字)9783030954673

ISBN: (纸本)9783030954673;9783030954666

Convolutional neural networks (CNNs) achieve state-of-the-art results in many application areas, including image classification. For some applications it would be useful but impractical to deploy them on mobile devices with limited memory and power. A currently active area of research is the compression of deep networks while maintaining accuracy, with the aim of reducing memory usage, energy consumption and processing time. Several network compression methods have been proposed and have achieved good results, but they usually require the specification of parameters and are computationally expensive. We propose a new fast automated method called Unsupervised PulseNet that uses unsupervised k-means clustering to detect clusters of similar filters, and nodes in fully-connected layers, and prunes those that are redundant. We evaluate it on the CIFAR10, CIFAR100 and Tiny-imagenet datasets using Alexnet, VGG16 and a 2-layer CNN called CifarNet suggested by the Tensorflow group. Compared to other methods in the literature we achieve the greatest compression, in shorter times, and with negligible loss in classification accuracy. In particular, we reduced Alexnet down to less than 0.7% of its original size, while not losing more than 2% classification accuracy.

关键词： Pruning networks Network compression Convolutional neural Network image Recognition Classification

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The Effects of Infrared Sensor Wavelength on Panchromatic image Colorization Performance

The Effects of Infrared Sensor Wavelength on Panchromatic Im...

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IEEE Signal processing and Communications applications (SIU)

作者： Süleyman Emir Akın Toygar Akgün Bilgisayar Mühendisliği Bölümü TOBB Ekonomi ve Teknoloji Üniversitesi Ankara Türkiye

ISBN: (数字)9798350388961

ISBN: (纸本)9798350388978

Infrared (IR) imaging sensors, designed to detect the wavelength range between $0.9 \mu \mathrm{m}$ and $14 \mu \mathrm{m}$, offer unique advantages over daylight cameras in consumer, industrial, and defense applications. However, IR images lack natural color information and can be challenging for individuals without sensor-specific training to interpret. Consequently, transforming IR images into perceptually realistic color images represents a valuable research endeavor with significant commercial potential. Recently, various studies utilizing deep neural networks for colorizing single-mode (near-IR or thermal) infrared images have been reported. This article will apply a common neural network architecture to images captured with different imaging modes (near-IR, thermal IR, and low-light) for colorization and compare the results. These experiments will examine the influence of perceived wavelength on the colorization process.

关键词： Training image color analysis Infrared image sensors Color artificial neural networks Signal processing Infrared sensors

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Revisiting DCT in Deep Learning Era: An Initial Denoising Application

Revisiting DCT in Deep Learning Era: An Initial Denoising Ap...

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IEEE Signal processing and Communications applications (SIU)

作者： Hasan H. Karaoğlu Ender M. Ekşioğlu Elektronik ve Haberle¸sme Mühendisli˘gi Bölümü İstanbul Teknik Üniversitesi İstanbul Türkiye

ISBN: (数字)9798350388961

ISBN: (纸本)9798350388978

In the classical era of image denoising, the methods working in transform domain have achieved high performance results. However, most of the deep neural networks that have been proposed in the last decade and have shown better noise removal performance try to denoise the noisy image in pixel domain. In deep learning literature, there are few deep neural networks that work in transform domain. Most of them have not chosen the discrete cosine transform (DCT), which is known to provide a very good representation for most images. This is the result of convolution layer, which is often used in deep networks, searching in vain for a relationship between neighboring values of an image’s uncorrelated global and block DCT coefficients. On the other hand, it is known that working with transform coefficients of overlapped image blocks improves noise removal performance. Recent studies have shown that the convolution of an image with 2D DCT basis images is a meaningful ordering of the DCT coefficients of overlapping image blocks. Hence, in this paper, a deep neural network is proposed to remove noise in the DCT domain. Experiments on color images indicate that the proposed network is quantitatively and qualitatively successful in noise removal.

关键词： Deep learning Convolution Noise Noise reduction Transforms artificial neural networks Color

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Mathematical Analysis of Human Motion Visual Capture image processing Based on artificial Intelligence

SSRN

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SSRN 2023年

作者： Yang, Xinbin Yichun University China

artificial intelligence's success in research and daily life is currently outstanding. This extensive essay will focus mainly on deep neural networks, the current "workhorse" of artificial intelligence. We will explore the unresolved concerns, lay out the main theoretical axes, and offer specific instances. During production, manufacturers must identify quality issues and halt the supply of faulty products to customers. We look on automating quality controls using deep neural networks instead of manual inspection procedures based on image processing. A deep neural network's ability to spot quality problems was tested and investigated with an industrial case study from the real world. Compared to the accuracy of 70-80% of a trained human inspector. In picture applications, deep neural networks are effective. In this study, an efficient method for image processing was devised using a mix of AI and mathematical modelling. © 2023, The Authors. All rights reserved.

关键词： Deep neural networks

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Optical Character Recognition of Persian Epigraphs by Using artificial neural networks in MATLAB

Optical Character Recognition of Persian Epigraphs by Using ...

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artificial Intelligence and Quantum Computation-Based Sensor Application (ICAIQSA), International Conference on

作者： Sumaiyya Khan Aashish Bardekar Dept. of Computer Science and Engineering Sipna College of Engineering & Technology Amravati India

ISBN: (数字)9798331517953

ISBN: (纸本)9798331517960

Persian, also known as Farsi, was once a dominant language and cultural influence worldwide. Today, it is nearly extinct in the Indian subcontinent. However, for centuries, Farsi played a major role in high culture and administration across the subcontinent. Persian characters are complex, with various features such as ascenders, descenders, vertical lines, and curves. Automatic translation of Persian characters from inscriptions into English is currently unavailable. The process of deciphering and translating inscribed text in epigraphy can be time-consuming. This paper specifically focuses on identifying Persian epigraphs. First, the inscription image is cleaned up, and images containing Persian characters are obtained. Using preprocessing techniques, the images are then prepared for input to artificial neural networks. The artificial neural networks are trained to effectively recognize various patterns of Persian characters, as they can learn complex patterns and adapt to changes in images, resulting in high accuracy in Persian character recognition. Finally, the proposed method's performance is evaluated using accuracy, precision, and speed metrics. The results demonstrate that the method accurately and rapidly detects Persian characters in various images. This research has the potential to advance Persian character recognition and image processing, enabling broader applications such as converting image documents into text, character recognition in documents, and text translation with Persian image content.

关键词： Training Measurement Translation Accuracy Optical character recognition artificial neural networks Feature extraction Mathematical models Character recognition MATLAB

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V-DETR: Pure Transformer for End-to-End Object Detection 16th

V-DETR: Pure Transformer for End-to-End Object Detection

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16th Asian Conference on Intelligent Information and Database Systems (ACIIDS)

作者： Dung Nguyen Van-Dung Hoang Van-Tuong-Lan Le Hue Univ Hue Univ Sci Hue City 530000 Vietnam HCMC Univ Technol & Educ Fac Informat Technol Ho Chi Minh City 720000 Vietnam Hue Univ Dept Acad & Students Affairs Hue City 530000 Vietnam

ISBN: (纸本)9789819749843;9789819749850

In the field of computer vision, the task of object detection is one of the important tasks, with many challenges and practical applications. Its task is to classify and determine the location of objects in images or videos. Many machine learning methods, especially deep learning, have been developed to perform this task. This article introduces a model combining DETR (DEtection TRansfomer) and ViT (Vision Transformer) as a method to recognize objects in images/videos that only use components of the Transformer model. The DETR model achieves good results in object detection using the Transformer architecture and without the need for complex intermediate steps. The ViT model, a Transformer-based architecture, has brought about a breakthrough in image classification. Combining both architectures opens exciting prospects in computer vision. The input image automatically extracted features using the ViT model previously trained on the imageNet21K dataset, then the features will be fed into the Transformer model to find the classification and bounding box of the objects. Experimental results on test data sets showthat this combined model has better ability in object recognition than DETR and ViT alone. This brings important prospects for the application of the Transformer model not only in the field of natural language processing but also in the field of image classification and object detection. The results of our proposed model have quite high mAP@0.5= 0.444 accuracy, slightly better than the original DETR model. The code is available at https://***/nguyendung622/vitdetr.

关键词： Computer Vision Object Detection Classification Convolutional neural networks Deep Learning DETR ViT

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Comparison of deep convolutional neural network models with OCT images for dental caries classification

Comparison of deep convolutional neural network models with ...

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Conference on Medical Imaging - Biomedical applications in Molecular, Structural, and Functional Imaging

作者： Salehi, Hassan S. Granados, Andreina Mahdian, Mina Calif State Univ Chico Dept Elect & Comp Engn Chico CA 95929 USA Gen Elect Healthcare Med Imaging Madison WI USA SUNY Stony Brook Sch Dent Med Stony Brook NY 11794 USA

ISBN: (数字)9781510649484

ISBN: (纸本)9781510649484;9781510649477

Deep learning techniques specifically deep convolutional neural network (CNN) models, the latest core model of artificial neural networks, provide computer-vision capabilities, including medical and biomedical image analysis and classification. In this paper, we imaged ex vivo human tooth specimens using OCT imaging systems to classify and clarify the accuracy of different tooth samples with and without carious lesions via deep CNN models with transfer-learning and fine-tuning strategies. Collecting a large amount of OCT image data from dental samples can be difficult, and not providing sufficient data for CNN models can lead to overfitting. For these reasons, transfer learning and fine-tuning techniques were utilized in this study. OCT images of human extracted premolar and molar teeth were categorized into three classes. Five deep CNN models, specifically, a basic CNN with three convolutional and max pooling layers, VGG16 and VGG19 transfer-learning models, and finely tuned VGG16 and VGG19 models were developed and evaluated for OCT image classification of dental caries. In transfer learning, an existing learned model was employed as a feature extractor without changing the weight data, while in fine tuning, an existing learned model was utilized as a feature extractor by relearning some of the weight data. These methods are powerful methods for training deep CNN models without overfitting. This study highlights the performance of various deep learning models for OCT image classification of carious lesions.

关键词： deep learning convolutional neural networks image processing optical coherence tomography classification dental caries

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Guest Editorial Cognitive Cyber-Physical Systems With AI Based Solutions in Medical Informatics

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2023年第2期27卷 586-587页

作者： Sangaiah, Arun Kumar Wang, Xizhao Lin, Yi-Bing Jianwei, Niu Yuan, Xiaohui Natl Yunlin Univ Sci & Technol Touliu Taiwan Shenzhen Univ Shenzhen Peoples R China Natl Chiao Tung Univ NCTU Hsinchu 30050 Taiwan Beihang Univ Beijing 10091 Peoples R China Univ North Texas Denton TX USA

All six papers in this special section engage in different streams but extremely relevant domain vectors of Cognitive Cyber-Physical Systems (CCPS) with artificial intelligence (AI) based solutions in medical informatics. Highlights recent trends in the scientific community and presents emergent technologies, implementations, applications concerning the CPSS. CPSS is witnessing rapid transformation as an interdisciplinary technology that blends physical components and computing devices to enable AI-based solutions. CCPS will be playing a significant role that integrates machine learning/AI techniques and resulting in dramatic improvements for medical informatics and the future of human-augmentation. CPHMS coordinates supervisory medical systems and medical resources everywhere; there is a great scope towards health consciousness and healthy society. Medical Cyber-Physical Systems (MCPS) in healthcare towards critical integration in network of medical devices. MCPS is the next generation computing that is comprised of tightly coupled computational and communication components of medical automation systems such as clinical decision, early detection of health infectious, disease prevention, rapid analysis of health hazards and so on. CCPS and MCPS research would be created new models, new design, and integration models for large scale systems in comprehensive, holistic medical automation systems. With recent enlargements in the big data processing, cognitive data science and AI, it is now possible to create even more realistic digital twins that properly model different operating situations and characteristics to process the medical intelligence systems.

关键词： Special issues and sections Cyber-physical systems artificial intelligence Bioinformatics Cognition Medical services Machine learning Data augmentation neural networks Biomedical imaging InterPlanetary File System image segmentation Analytical models

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Exploring Meibomian Gland Dysfunction Grading: A Comparison of Machine Learning and XAI Approaches 2

Exploring Meibomian Gland Dysfunction Grading: A Comparison ...

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2nd IEEE International Conference on image processing and Computer applications, ICIPCA 2024

作者： Liang, Congxiao Wang, Mini Han Liu, Haoyang Chong, Kelvin K.L. Beijing Institute of Technology Center for Sport Science and Physical Education Zhuhai519000 China The Department of Ophthalmology and Visual Sciences Chinese University of Hong Kong 999077 Hong Kong The Faculty of Data Science City University of Macau Macau999078 China The Center of Big Data and Ai Zhuhai Institute of Advanced Technology Chinese Academy of Sciences Zhuhai519000 China The R&d Center of Ai and Digital Medicine Guangdong Digital Medicine Promotion Science and Technology Co. Ltd Guangdong 519000 China Guangdong Medical University Dongguan China Zhuhai Institute of Advanced Technology Chinese Academy of Sciences Zhuhai China Perspective Technology Group Zhuhai China The Faculty of Medicine Chinese University of Hong Kong Hong Kong

ISBN: (纸本)9798350360240

This research investigates the effectiveness of employing a machine learning methodology and an Explainable artificial Intelligence (XAI) tool for the assessment of Meibomian Gland Dysfunction (MGD). Utilizing ResNet34, a convolutional neural network (CNN), trained on a diverse dataset comprising images depicting varying degrees of MGD severity, the machine learning approach endeavors to classify MGD severity into four distinct grades (0, 1, 2, 3) by leveraging extracted characteristic features. Findings reveal comparable accuracy between ResNet34 and the XAI tool, with ResNet34 achieving a slightly lower accuracy of 99.1% in contrast to the XAI tool's 99.4%. However, ResNet34 demonstrates marginally higher precision and F1-score at 98.2% and 98.45%, respectively, suggesting a nuanced advantage in precision-recall equilibrium. Conversely, the XAI tool maintains a commendable precision of 97.2% and an Fl-score of 97.94%, alongside a recall of 98.7%, underscoring its effectiveness in discerning positive instances. Noteworthy is that ResNet34 operates as a black-box model, providing limited interpretability, while the XAI tool emphasizes transparency by furnishing clinicians with understandable MGD grading elucidations derived from meibomian gland segmentation and atrophy analysis. This transparency enhances trust and comprehension, which are integral for clinical acceptance and decision-making. Furthermore, the utilization of XAI heatmap generation facilitates data quality enhancement, as evidenced by instances of erroneous artifact identification, emphasizing the imperative nature of artifact elimination for the refinement of MGD grading procedures. © 2024 IEEE.

关键词： Convolutional neural networks

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PENet: Prior evidence deep neural network for bladder cancer staging

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METHODS 2022年第0期207卷 20-28页

作者： Zhou, Xiaoqian Yue, Xiaodong Xu, Zhikang Denoeux, Thierry Chen, Yufei Shanghai Univ Sch Comp Engn & Sci Shanghai Peoples R China Artificial Intelligence Inst Shanghai Univ Shanghai Peoples R China Shanghai Univ Sino European Sch Technol Shanghai Peoples R China Univ technol Compiegne Compiegne France Tongji Univ Coll Elect & Informat Engn Shanghai Peoples R China

Bladder cancer is a heterogeneous, complicated, and widespread illness with high rates of morbidity, death, and expense if not treated adequately. The accurate and exact stage of bladder cancer is fundamental for treatment choices and prognostic forecasts, as indicated by convincing evidence from randomized trials. The extraordinary capability of Deep Convolutional neural networks (DCNNs) to extract features is one of the primary advantages offered by these types of networks. DCNNs work well in numerous real clinical medical applications as it de-mands costly large-scale data annotation. However, a lack of background information hinders its effectiveness and interpretability. Clinicians identify the stage of a tumor by evaluating whether the tumor is muscle-invasive, as shown in images by the tumor's infiltration of the bladder wall. Incorporating this clinical knowledge in DCNN has the ability to enhance the performance of bladder cancer staging and bring the prediction into accordance with medical principles. Therefore, we introduce PENet, an innovative prior evidence deep neural network, for classifying MR images of bladder cancer staging in line with clinical knowledge. To do this, first, the degree to which the tumor has penetrated the bladder wall is measured to get prior distribution parameters of class probability called prior evidence. Second, we formulate the posterior distribution of class probability according to Bayesian Theorem. Last, we modify the loss function based on posterior distribution of class probability which parameters include both prior evidence and prediction evidence in the learning procedure. Our investigation reveals that the prediction error and the variance of PENet may be reduced by giving the network prior evidence that is consistent with the ground truth. Using MR image datasets, experiments show that PENet performs better than image-based DCNN algorithms for bladder cancer staging.

关键词： Bladder cancer staging Deep neural network Prior evidence fusion

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