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deep learning Approaches for Morphological Classification of Intestinal Organoids

IEEE ACCESS 2025年 13卷 62267-62287页

作者： Cicceri, Giovanni Di Bella, Sebastiano Di Franco, Simone Stassi, Giorgio Todaro, Matilde Vitabile, Salvatore Univ Palermo Dept Biomed Neurosci & Adv Diagnost BiND I-90127 Palermo Italy Univ Palermo Dept Precis Med Med Surg & Crit Care MePreCC I-90127 Palermo Italy Univ Palermo Dept Hlth Promot Sci Internal Med & Med Specialties ProMISE I-90127 Palermo Italy

Organoids, derived from primary donor or stem cells, closely replicate the composition and function of their in vivo counterparts. This quality makes them a reliable model for validating hypotheses on disease-related biological processes and mechanisms. To date, the classification of organoids is performed manually by microscope and, therefore, in a data-driven application, is time-consuming, inaccurate, and difficult to morphological analysis process. The use of deep learning (DL) in organoid image analysis becomes crucial to handle complexity, variability, and large amounts of data efficiently and accurately, overcoming the limitations of traditional image processing approaches. In this paper, five CNN-based DL models such as MobileNet, DenseNet, ResNet, Inception, VGG, and the very recent Vision Transformers (ViT) were analyzed using a publicly available dataset for the morphological classification of intestinal organoids. Additionally, traditional ML models, such as SVM and RF, were tested for comparison using a feature set similar to conventional image processing tools. The systematic performance evaluation is designed to guide users in choosing the most suitable model for processing organoid images. Among all models, ViT achieved the highest accuracy of 86.95%, demonstrating its effectiveness in organoid classification. Inception and DenseNet also exhibited strong performance, with accuracy values of 86.10% and 86.47%, respectively. Rather, SVM and RF performed significantly worse, showing an accuracy approximately 20% lower than the selected DL models. Considering efficiency, ViT had the highest accuracy but required more resources (0.0437 sec/image, 343 MB), while MobileNet, the lightest model (35.6 MB), had the fastest inference time (0.0063 sec/image). The findings highlight the potential of DL models in enhancing the accuracy of organoid classification while emphasizing the importance of balancing performance with computational efficiency for real-time ap

关键词： Organoids Accuracy Tumors Computational modeling Analytical models Morphology Biological system modeling Transformers Three-dimensional displays Support vector machines deep learning image classification intestinal organoids stem cells cancer

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TrioConvTomatoNet: A robust CNN architecture for fast and accurate tomato leaf disease classification for real time application

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SCIENTIA HORTICULTURAE 2024年 330卷

作者： Vini, S. Ledbin Rathika, P. PSN Coll Engn & Technol ECE Dept Tirunelveli 627152 India

Tomato leaf diseases are a common threat to long-term tomato protection that affects many farmers worldwide. Computer-assisted technology is more prevalent for early and accurate diagnosis of tomato leaf diseases, which may reduce the likelihood that the plant will suffer further harm. The advancement of deep learning techniques has a lot of potential for accurate classification of leaf diseases through an automated feature extraction topology. By changing the network layers and their parameters in the feature extraction topology, it is possible to extract the exact features and improve the classification accuracy. In this study, TrioConvTomatoNet, novel deep convolutional neural network architecture, is proposed, which includes a 3-series convolution layer under each stage of the feature extraction topology to capture and integrate the information from the leaf images efficiently. To improve the network's learning ability and perform accurate classifications, the proposed method incorporates the stochastic gradient descent optimizer. In order to facilitate proper learning, a separate dataset preparation strategy was followed for tomato leaf image dataset collection, which includes existing and real-time images. This research is carried out with varying the number of convolution layers to express the effectiveness of the proposed method in capturing and integrating information from the leaf images. Also, the extensive experiments on an unseen dataset collection with and without augmentation express the superiority and robustness of TrioConvTomatoNet in terms of both accuracy and speed when compared to state-of-the-art methods. The proposed method achieves remarkable accuracy of about 99.39% in disease classification while processing both existing database and real-time images, making it suitable for practical deployment in agricultural settings.

关键词： image processing Tomato leaf analysis Disease classification Smart agriculture deep learning Convolutional neural network

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real-time 3-D Measurement With Dual-Frequency Fringes by deep learning

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IEEE SENSORS JOURNAL 2024年第10期24卷 16576-16586页

作者： Shen, Siyuan Lu, Rongsheng Wan, Dahang Yin, Jiajie He, Pan Hefei Univ Technol Dept Instrument Sci & Optoelect Engn Hefei 230009 Peoples R China

Fringe projection technology is a commonly used technique in optical 3-D measurement. In high-speed motion scenarios, due to image noise and the effects of object motion, projecting more fringe patterns for high-precision phase unwrapping is a common method, which can significantly reduce the frame rate of 3-D reconstruction. deep learning techniques have been employed for high-precision phase unwrapping, but typically, these models have a large parameter and computation, making them difficult to integrate into real-time 3-D reconstruction systems. In this article, we first employ the lookup table (LUT) technique for rapid computation of dual-frequency phases. Second, we design a deep learning model with a parameter size of only 276 kb for high-precision phase unwrapping and quickly embed it into a real-time 3-D reconstruction system through 8-bit quantization without compromising accuracy. Furthermore, we utilize the calibration parameters of a real fringe projection profilometry (FPP) system to establish a corresponding virtual FPP system for rapid generation of data required for model training. Finally, we optimize the generation of point clouds by avoiding the computationally slow inverse matrix operation process. Experiments show that our model can achieve high-precision real-time 3-D reconstruction at a rate of 130 frames/s.

关键词： Table lookup deep learning Three-dimensional displays real-time systems Computational modeling Sensors Noise 3-D displays convolutional neural networks (CNNs) dynamic scene fringe projection phase unwrapping

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Semantic-Based Optimization of deep learning for Efficient real-time Medical image Segmentation

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INTERNATIONAL JOURNAL ON SEMANTIC WEB AND INFORMATION SYSTEMS 2024年第1期20卷 1页

作者： Wei, Zhenkun Liu, Jia Yao, Yu Chinese Acad Sci Chengdu Inst Comp Applicat Chengdu Peoples R China Univ Chinese Acad Sci Beijing Peoples R China China Mobile Ind Res Inst Beijing Peoples R China

In response to the critical need for advanced solutions in medical imaging segmentation, particularly for real-time applications in diagnostics and treatment planning, this study introduces SM-UNet. This novel deep learning architecture efficiently addresses the challenge of real-time, accurate medical image segmentation by integrating convolutional neural network (CNN) with multilayer perceptron (MLP). The architecture uniquely combines an initial convolutional encoder for detailed feature extraction, MLP module for capturing long-range dependencies, and a decoder that merges global features with high-resolution CNN map. Further optimization is achieved through a tokenization approach, significantly reducing computational demands. Its superior performance is confirmed by evaluations on standard datasets, showing interaction times drastically lower than comparable networks-between 1/6 to 1/10, and 1/25 compared to SOTA models. These advancements underscore SM-UNet's potential as a groundbreaking tool for facilitating real-time, precise medical diagnostics and treatment strategies.

关键词： Computational Efficiency Convolutional Neural Networks deep learning Optimization Healthcare System Magnetic Resonance Imaging Multilayer Perceptron

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deep learning-based instance segmentation on 3D laser triangulation data for inline monitoring of particle size distributions in construction and demolition waste recycling

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RESOURCES CONSERVATION AND RECYCLING 2024年 205卷

作者： Wu, Xiaoye Kroell, Nils Greiff, Kathrin Rhein Westfal TH Aachen Dept Anthropogen Mat Cycles Wuellnerstr 2 D-52062 Aachen Germany

Overlapping material flow presentations in construction and demolition waste (CDW) recycling make an inline particle size distribution (PSD) monitoring challenging. Here, we aim to build a deep-learning-based segmentation model for overlapping particles in 3D -laser -triangulation images of CDW. Our model was trained on three specially designed datasets with two transfer learning processes. U -net was employed as the backbone and MultiStar algorithm was used to describe particle shapes. The final model demonstrated an impressive performance on test set, with a mean average precision (mAP) of 92.8% at IoU = 0.5. Comparing with the traditional segmentation algorithm based on image processing methods, the mAP can only reach to 27.4% on the same images. The shown model performance paves the way toward novel sensor technology applications for real -time PSD monitoring in CDW recycling.

关键词： Sensor-based material flow characterization Digital image processing deep learning Artificial intelligence Instance segmentation Circular economy

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deep Adaptive Phase learning: Enhancing Synthetic Aperture Sonar imagery Through Learned Coherent Autofocus

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2024年 17卷 9517-9532页

作者： Gerg, Isaac D. Cook, Daniel A. Monga, Vishal Kitware Inc Clifton Pk NY 12065 USA Georgia Tech Res Inst Smyrna GA 30080 USA Penn State Univ Sch EECS University Pk PA 16802 USA

Having well-focused synthetic aperture sonar (SAS) imagery is important for its accurate analysis and support of autonomous systems. Despite advances in motion estimation and image formation methods, there persists a need for robust autofocus algorithms deployed both topside and in situ embedded in unmanned underwater vehicles (UUVs) for real-time processing. This need stems from the fact that systematic focus errors are common in SAS and often result from misestimating sound speed in the medium or uncompensated vehicle motion. In this article, we use an SAS-specific convolutional neural network (CNN) to robustly and quickly autofocus SAS images. Our method, which we call deep adaptive phase learning (DAPL), explicitly utilizes the relationship between the $k$-space domain and the complex-valued SAS image to perform the autofocus operation in a manner distinctly different than existing optical image deblurring techniques that solely rely on magnitude-only imagery. We demonstrate that DAPL mitigates three types of systematic phase errors common to SAS platforms (and combinations thereof): quadratic phase error (QPE), sinusoidal error, and sawtooth error (i.e., yaw error). We show results for DAPL against a publicly available, real-world high-frequency SAS dataset, and also compare them against several existing techniques including phase gradient autofocus (PGA). Our results show that DAPL is competitive with or outperforms state-of-the-art alternatives without requiring manual parameter tuning.

关键词： Synthetic aperture sonar Electronics packaging Systematics Synthetic aperture radar Sea floor Apertures real-time systems deep learning image enhancement synthetic aperture sonar

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On-road obstacle detection in real time environment using an ensemble deep learning model

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SIGNAL image AND VIDEO processing 2024年第6-7期18卷 5387-5400页

作者： Balasundaram, Ananthakrishnan Shaik, Ayesha Prasad, A. Pratheepan, Yogarajah Vellore Inst Technol Ctr Cyber Phys Syst Chennai Tamil Nadu India Vellore Inst Technol VIT Sch Comp Sci & Engn Chennai Tamil Nadu India Ulster Univ Sch Comp Engn & Intelligent Syst Londonderry North Ireland

Obstacle detection on road is a challenging task in autonomous vehicle driving. Although obstacle detection is carried out with the help of sensors which are accurate and precise in real-time, they are not cost effective and computationally intensive. So, a computer vision and deep learning-based approach can be considered as a potential alternative. The proposed system is an ensemble of two instance segmentation algorithms namely You Only Look Once v7 (YOLOv7) and Mask Region-Based Convolutional Neural Network (Mask R-CNN) used to detect various obstacles on road. The system was tested on popular obstacle detection datasets such as INRIA, KAIST, and COCO2017. The ensemble model achieved mean average precision score of 90.1, 95.4 and 88.9 and mean intersection over union score of 93.3, 89.7 and 91.4 for KAIST, INRIA and COCO datasets respectively. A custom dataset for detecting obstacles was developed and the proposed model was tested on the custom dataset as well.

关键词： Obstacle detection YOLOV7 Mask R-CNN deep learning Ensemble model

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Distributed deep learning for Medical image processing in Cloud Environments

Distributed Deep Learning for Medical Image Processing in Cl...

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Artificial Intelligence for Innovations in Healthcare Industries (ICAIIHI), International Conference on

作者： Neeraj Varshney Parul Madan Anurag Shrivastava C Praveen Kumar A Kakoli Rao Amit Srivastava Department of Computer Engineering and Applications GLA University Mathura Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamilnadu Department of Computer Science and Engineering Institute of Aeronautical Engineering Hyderabad Telangana Lloyd Institute of Engineering and Technology Greater Noida Lloyd Law College Greater Noida

The incorporation of distributed deep learning for medical image processing in cloud settings is the subject of this study. The findings demonstrate the high viability and significant performance advantages realized by cloud-based distributed systems, notably significant processing time savings, outstanding diagnostic accuracy, as well as improved scalability. The consequences for security and privacy have been discussed, with a focus on effective safeguards for private medical information. There is a void in the literature about resource and cost-effectiveness optimization tactics used in cloud-based systems. Future research must concentrate on resource optimization tactics for economic sustainability, study developing security risks and privacy techniques, and incorporate real-world implementations in order to improve this topic. This study informs the use of distributed deep learning in cloud-based medical image processing as well as adds to the body of knowledge in healthcare technology.

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Streaming traffic classification: a hybrid deep learning and big data approach

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2024年第4期27卷 5165-5193页

作者： Seydali, Mehdi Khunjush, Farshad Dogani, Javad Shiraz Univ Sch Elect & Comp Engn Dept Comp Sci & Engn & IT Mollasadara St Shiraz *** Iran

Massive amounts of real-time streaming network data are generated quickly because of the exponential growth of applications. Analyzing patterns in generated flow traffic streaming offers benefits in reducing traffic congestion, enhancing network management, and improving the quality of service management. processing massive volumes of generated traffic poses more challenges when data traffic encryption is raised. Classifying encrypted network traffic in real-time with deep learning networks has received attention because of their excellent performance. The substantial volume of incoming packets, characterized by high speed and wide variety, puts real-time traffic classification within the domain of big data problems. Classifying traffic with high speed and accuracy is a significant challenge in the era of big data. The real-time nature of traffic intensifies deep learning networks, necessitating a considerable number of parameters, layers, and resources for optimal network training. Until now, various datasets have been employed to evaluate the effectiveness of previous methods for classifying encrypted traffic. The primary objective has been to enhance accuracy, precision, and F1-measure. Presently, encrypted traffic classification performance depends on pre-existing datasets. The learning and testing phases are done offline, and more research is needed to investigate the feasibility of these methods in real-world scenarios. This paper examines the possibility of a tradeoff between evaluating the model's effectiveness, execution time, and utilization of processing resources when processing stream-based input data for traffic classification. We aim to explore the feasibility of establishing a tradeoff between these factors and determining optimal parameter settings. This paper used the ISCX VPN-Non VPN 2016 public dataset to evaluate the proposed method. All packets from the dataset were streamed continuously through Apache Kafka to the classification framework. Num

关键词： deep learning Encrypted traffic Streaming network Traffic classification

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real time Performance Evaluation of deep learning Algorithms in image Recognition under the Pytorch Framework

Real Time Performance Evaluation of Deep Learning Algorithms...

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2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems, IACIS 2024

作者： Lu, Di Liu, Shengzhi Xinjiang College of Science & Technology Korla China

ISBN: (纸本)9798350360660

With the rapid development of artificial intelligence technology, deep learning has become one of the key technologies in the field of image recognition. PyTorch has become the preferred framework for researchers due to its flexibility, efficiency, and ease of use. This study focuses on the real-time performance of deep learning algorithms in image recognition under the PyTorch framework, and its effectiveness is verified through system experiments. The experiment revealed that the algorithm under this framework exhibits excellent real-time performance in image recognition tasks, with an average frame rate of up to 59.46 FPS and an image processing delay as low as 59.44 milliseconds, fully meeting the demand for efficient processing in a wide range of application scenarios. This discovery demonstrates the powerful potential and practicality of PyTorch in the field of image recognition. © 2024 IEEE.

关键词： Adversarial machine learning

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