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Journal articles on the topic 'NET architecture'

Author: Grafiati

Published: 7 June 2025

Last updated: 16 July 2025

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1

V, Slyusar. "Inverse Architecture U-Net – InvU-Net." Artificial Intelligence 29, AI.2024.29(4) (2024): 115–32. https://doi.org/10.15407/jai2024.04.115.

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The article proposes an inverse architecture of the U-Net neural network, named InvU-Net, which differs from the traditional scheme by increasing the dimensionality of images during the initial stages of processing. A comparison was conducted between two approaches for increasing image resolution: UpSampling2D layers and transposed Conv2DTranspose convolutional layers. The latter demonstrated superior results due to its ability to learn weighting coefficients. As part of the study, several InvU-Net modifications were developed and tested: Small, Medium, and Large, differing in structural compl

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Stevenson, I. ".Net core architecture." Computing and Control Engineering 14, no. 5 (2003): 24–27. http://dx.doi.org/10.1049/cce:20030505.

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Eisenstat, Joshua, Matthias W. Wagner, Logi Vidarsson, Birgit Ertl-Wagner, and Dafna Sussman. "Fet-Net Algorithm for Automatic Detection of Fetal Orientation in Fetal MRI." Bioengineering 10, no. 2 (2023): 140. http://dx.doi.org/10.3390/bioengineering10020140.

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Identifying fetal orientation is essential for determining the mode of delivery and for sequence planning in fetal magnetic resonance imaging (MRI). This manuscript describes a deep learning algorithm named Fet-Net, composed of convolutional neural networks (CNNs), which allows for the automatic detection of fetal orientation from a two-dimensional (2D) MRI slice. The architecture consists of four convolutional layers, which feed into a simple artificial neural network. Compared with eleven other prominent CNNs (different versions of ResNet, VGG, Xception, and Inception), Fet-Net has fewer arc

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Iqbal, Shahzaib, Syed S. Naqvi, Haroon A. Khan, Ahsan Saadat, and Tariq M. Khan. "G-Net Light: A Lightweight Modified Google Net for Retinal Vessel Segmentation." Photonics 9, no. 12 (2022): 923. http://dx.doi.org/10.3390/photonics9120923.

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In recent years, convolutional neural network architectures have become increasingly complex to achieve improved performance on well-known benchmark datasets. In this research, we have introduced G-Net light, a lightweight modified GoogleNet with improved filter count per layer to reduce feature overlaps, hence reducing the complexity. Additionally, by limiting the amount of pooling layers in the proposed architecture, we have exploited the skip connections to minimize the spatial information loss. The suggested architecture is analysed using three publicly available datasets for retinal vesse

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Polattimur, Rukiye, Mehmet Süleyman Yıldırım, and Emre Dandıl. "Fractal-Based Architectures with Skip Connections and Attention Mechanism for Improved Segmentation of MS Lesions in Cervical Spinal Cord." Diagnostics 15, no. 8 (2025): 1041. https://doi.org/10.3390/diagnostics15081041.

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Background/Objectives: Multiple sclerosis (MS) is an autoimmune disease that damages the myelin sheath of the central nervous system, which includes the brain and spinal cord. Although MS lesions in the brain are more frequently investigated, MS lesions in the cervical spinal cord (CSC) can be much more specific for the diagnosis of the disease. Furthermore, as lesion burden in the CSC is directly related to disease progression, the presence of lesions in the CSC may help to differentiate MS from other neurological diseases. Methods: In this study, two novel deep learning models based on fract

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Camacho-Gonzalez, Francisco David, Nestor Andres Garcia-Rojas, José Francisco Martínez-Trinidad, and Jesús Ariel Carrasco-Ochoa. "Simplified LSL-Net Architecture for Unmanned Aerial Vehicle Detection in Real-Time." Technologies 13, no. 5 (2025): 177. https://doi.org/10.3390/technologies13050177.

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Given the growth of unmanned aerial vehicles (UAVs), their detection has become a recent and complex problem. The literature has addressed this problem by applying traditional computer vision algorithms and, more recently, deep learning architectures, which, while proven more effective than previous ones, are computationally more expensive. In this paper, following the approach of applying deep learning architectures, we propose a simplified LSL-Net-based architecture for UAV detection. This architecture integrates the ability to track and detect UAVs using convolutional neural networks. The b

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Andersen, H. C., F. C. Teng, and A. C. Tsoi. "Single net indirect learning architecture." IEEE Transactions on Neural Networks 5, no. 6 (1994): 1003–5. http://dx.doi.org/10.1109/72.329701.

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Finlayson, Graham, and Jake McVey. "TM-Net: A Neural Net Architecture for Tone Mapping." Journal of Imaging 8, no. 12 (2022): 325. http://dx.doi.org/10.3390/jimaging8120325.

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Tone mapping functions are applied to images to compress the dynamic range of an image, to make image details more conspicuous, and most importantly, to produce a pleasing reproduction. Contrast Limited Histogram Equalization (CLHE) is one of the simplest and most widely deployed tone mapping algorithms. CLHE works by iteratively refining an input histogram (to meet certain conditions) until convergence, then the cumulative histogram of the result is used to define the tone map that is used to enhance the image. This paper makes three contributions. First, we show that CLHE can be exactly form

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Rehman, Mobeen Ur, SeungBin Cho, Jee Hong Kim, and Kil To Chong. "BU-Net: Brain Tumor Segmentation Using Modified U-Net Architecture." Electronics 9, no. 12 (2020): 2203. http://dx.doi.org/10.3390/electronics9122203.

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The semantic segmentation of a brain tumor is of paramount importance for its treatment and prevention. Recently, researches have proposed various neural network-based architectures to improve the performance of segmentation of brain tumor sub-regions. Brain tumor segmentation, being a challenging area of research, requires improvement in its performance. This paper proposes a 2D image segmentation method, BU-Net, to contribute to brain tumor segmentation research. Residual extended skip (RES) and wide context (WC) are used along with the customized loss function in the baseline U-Net architec

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Turk, Fuat. "RNGU-NET: a novel efficient approach in Segmenting Tuberculosis using chest X-Ray images." PeerJ Computer Science 10 (February 5, 2024): e1780. http://dx.doi.org/10.7717/peerj-cs.1780.

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Tuberculosis affects various tissues, including the lungs, kidneys, and brain. According to the medical report published by the World Health Organization (WHO) in 2020, approximately ten million people have been infected with tuberculosis. U-NET, a preferred method for detecting tuberculosis-like cases, is a convolutional neural network developed for segmentation in biomedical image processing. The proposed RNGU-NET architecture is a new segmentation technique combining the ResNet, Non-Local Block, and Gate Attention Block architectures. In the RNGU-NET design, the encoder phase is strengthene

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Ramdan, Hendri, Moh Arief Soeleman, Purwanto Purwanto, Bahtiar Imran, and Ricardus Anggi Pramunendar. "Semantic segmentation of pendet dance images using multires U-Net architecture." ILKOM Jurnal Ilmiah 14, no. 3 (2022): 329–38. http://dx.doi.org/10.33096/ilkom.v14i3.1316.329-338.

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As a cultural heritage, traditional dance must be protected and preserved. Pendet dance is a traditional dance from Bali, Indonesia. Dance recognition raises a complex problem for computer vision research because the features representing the dancer must focus on the dancer's entire body. This can be done by performing a segmentation task process. One type of segmentation task in computer vision is the semantic segmentation. Mask R-CNN and U-NET were employed in this task. Since it was first introduced in 2015, semantic segmentation using the U-Net architecture has been widely adopted, develop

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Muller, Brook. "New Horizons for Sustainable Architecture." Nature and Culture 13, no. 2 (2018): 189–207. http://dx.doi.org/10.3167/nc.2018.130201.

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In this article, I explore conceptual strategies encouraging an ecologically responsive, water-centric approach to architectural design, such that design interventions become nature/culture hybrids connecting urban dwellers to larger hydrological conditions. I consider the notion of horizons as one mechanism for working out a trajectory for sustainable architecture, one that highlights experiential and environmental concerns simultaneously. In a conceptual shift, theorist David Leatherbarrow’s treatment of “three architectural horizons” (the equipmental—the objects of one’s immediate setting;

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Saha, Debasmita, Dr Ardhendu Mandal, and Rinku Ghosh. "MU Net: Ovarian Follicle Segmentation Using Modified U-Net Architecture." International Journal of Engineering and Advanced Technology 11, no. 4 (2022): 30–35. http://dx.doi.org/10.35940/ijeat.d3419.0411422.

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Ovaries play a pivotal role in production by generating eggs through oogenesis in the female reproductive system. This is one crucial aspect of reproduction as eggs are fertilized by the sperm which eventually leads to fertilization and eventually ending in embryo formation. Ovaries are often susceptible to diseases like infertility, polycystic ovarian syndrome (PCOS), ovarian cancer etc. Screening of ovarian follicles via ultrasound images can be of great help in the diagnosis of these abnormal situations. However, screening in most scenarios is still carried out manually by doctors and sonog

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Debasmita, Saha, Ardhendu Mandal Dr., and Ghosh Rinku. "MU Net: Ovarian Follicle Segmentation Using Modified U-Net Architecture." International Journal of Engineering and Advanced Technology (IJEAT) 12, no. 4 (2022): 30–35. https://doi.org/10.35940/ijeat.D3419.0411422.

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<strong>Abstract: </strong>Ovaries play a pivotal role in production by generating eggs through oogenesis in the female reproductive system. This is one crucial aspect of reproduction as eggs are fertilized by the sperm which eventually leads to fertilization and eventually ending in embryo formation. Ovaries are often susceptible to diseases like infertility, polycystic ovarian syndrome (PCOS), ovarian cancer etc. Screening of ovarian follicles via ultrasound images can be of great help in the diagnosis of these abnormal situations. However, screening in most scenarios is still carried out ma

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Mishra, Pragnyaban. "Crowd Counting Using CSR-Net Architecture." International Journal of Advanced Trends in Computer Science and Engineering 8, no. 6 (2019): 2762–67. http://dx.doi.org/10.30534/ijatcse/2019/13862019.

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Sineglazov, Victor, Olena Chumachenko, and Oleksandr Pokhylenko. "BAFUNet: Hybrid U-Net for Segmentation of Spine MR Images." Electronics and Control Systems 4, no. 82 (2024): 16–22. https://doi.org/10.18372/1990-5548.82.19365.

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The paper presents the development of a hybrid neural network architecture, BAFUNet, designed for the segmentation of spine MR images in the context of medical diagnostics. The architecture builds upon the classical U-Net, integrating atrous spatial pyramid pooling module in the bottleneck and a two-round fusion module in the skip connections to address challenges such as various object scales and unclear boundaries in medical images. The work describes the design of the proposed BAFUNet architecture, its implementation, and the experimental results. A comparative analysis was performed agains

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Anjaria, Kushal, and Arun Mishra. "Information Processing and Security Analysis of Shared System Resource Based Architectures." International Journal of Cooperative Information Systems 27, no. 04 (2018): 1850009. http://dx.doi.org/10.1142/s0218843018500090.

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Background: Nowadays, service-oriented architectures and cloud-based infrastructures are widely used in manufacturing industries and IT organizations. These architectures and infrastructures are based on shared system resources. In some organizations, system resources like a printer, photocopy machines, and scanners are also shared among the members of the organization. The purpose of the proposed work is to model various types of shared system resources, shared system resources based architecture/infrastructure and analyze the model to identify the possible security risk associated with share

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Farvin Sahapudeen, Dr Farjana, and Dr S. Krishna Mohan. "ENHANCED ATTENTION RESIDUAL PARALLEL U-NETS (EARPU) FOR LUNG TUMOR SEGMENTATION." Journal of Dynamics and Control 9, no. 3 (2025): 93–108. https://doi.org/10.71058/jodac.v9i3007.

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Precise identification and classification of diseased tissue and its adjacent healthy structures are vital in the diagnosis of conditions like lung cancer. Achieving a more accurate diagnosis necessitates a substantial amount of data. Yet, physicians often encounter challenges in manually analyzing extensive and intricate CT scan images to extract essential information. While UNet-based architectures have demonstrated superior performance in image segmentation compared to other deep learning architectures, challenges arise in segmentation accuracy due to the low resolution of medical images an

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Tjahyaningtijas, Hapsari Peni Agustin, Andi Kurniawan Nugroho, Cucun Very Angkoso, I. Ketut Edy Purnama, and Mauridhi Hery Purnomo. "Automatic Segmentation on Glioblastoma Brain Tumor Magnetic Resonance Imaging Using Modified U-Net." EMITTER International Journal of Engineering Technology 8, no. 1 (2020): 161–77. http://dx.doi.org/10.24003/emitter.v8i1.505.

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Glioblastoma is listed as a malignant brain tumor. Due to its heterogeneous composition in one area of the tumor, the area of tumor is difficult to segment from healthy tissue. On the other side, the segmentation of brain tumor MRI imaging is also erroneous and takes time because of the large MRI image data. An automated segmentation approach based on fully convolutional architecture was developed to overcome the problem. One of fully convolutional network that used is U-Net framework. U-Net architecture is evaluated base on the number of epochs and drop-out values to achieve the most suitable

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Kaczmarski, Peter. "WEB COMMUNICATION: IMPLEMENTING A RESTFUL WEB API IN C# .NET 8 USING CLEAN ARCHITECTURE." Communication & Cognition 57, no. 1-2 (2024): 3–42. http://dx.doi.org/10.57028/c57-003-z1057.

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Web communication technology forms today a crucial component of web- and cloud-based information processing. One of the key approaches in this area is REST (Fielding, 2000), which today coexists with other technologies, in particular GraphQL and gRPC. In this paper we focus on implementing REST-based web communication components using the latest version of C#/.NET SDK (.NET 8 LTS, released on 14.11.2023). In the first part, we discuss the concept of a RESTful network architecture and summarize the features which allow to define the communication style as RESTful. Next sections focus on C# impl

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Ohura, Norihiko, Ryota Mitsuno, Masanobu Sakisaka, et al. "Convolutional neural networks for wound detection: the role of artificial intelligence in wound care." Journal of Wound Care 28, Sup10 (2019): S13—S24. http://dx.doi.org/10.12968/jowc.2019.28.sup10.s13.

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Objective: Telemedicine is an essential support system for clinical settings outside the hospital. Recently, the importance of the model for assessment of telemedicine (MAST) has been emphasised. The development of an eHealth-supported wound assessment system using artificial intelligence is awaited. This study explored whether or not wound segmentation of a diabetic foot ulcer (DFU) and a venous leg ulcer (VLU) by a convolutional neural network (CNN) was possible after being educated using sacral pressure ulcer (PU) data sets, and which CNN architecture was superior at segmentation. Methods:

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Abdulfatah, Aliyu, Zhang Sheng, and Yirga Eyasu Tenawerk. "U-Net-Based Medical Image Segmentation: A Comprehensive Analysis and Performance Review." Journal of Electronic Research and Application 9, no. 1 (2025): 202–8. https://doi.org/10.26689/jera.v9i1.9450.

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Medical image segmentation has become a cornerstone for many healthcare applications, allowing for the automated extraction of critical information from images such as Computed Tomography (CT) scans, Magnetic Resonance Imaging (MRIs), and X-rays. The introduction of U-Net in 2015 has significantly advanced segmentation capabilities, especially for small datasets commonly found in medical imaging. Since then, various modifications to the original U-Net architecture have been proposed to enhance segmentation accuracy and tackle challenges like class imbalance, data scarcity, and multi-modal imag

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Radiuk, Pavlo. "Applying 3D U-Net Architecture to the Task of Multi-Organ Segmentation in Computed Tomography." Applied Computer Systems 25, no. 1 (2020): 43–50. http://dx.doi.org/10.2478/acss-2020-0005.

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AbstractThe achievement of high-precision segmentation in medical image analysis has been an active direction of research over the past decade. Significant success in medical imaging tasks has been feasible due to the employment of deep learning methods, including convolutional neural networks (CNNs). Convolutional architectures have been mostly applied to homogeneous medical datasets with separate organs. Nevertheless, the segmentation of volumetric medical images of several organs remains an open question. In this paper, we investigate fully convolutional neural networks (FCNs) and propose a

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Vockner, Sara, Matthias Mattke, Ivan M. Messner, et al. "Cone-Beam CT Segmentation for Intraoperative Electron Radiotherapy Based on U-Net Variants with Transformer and Extended LSTM Approaches." Cancers 17, no. 3 (2025): 485. https://doi.org/10.3390/cancers17030485.

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Artificial Intelligence (AI) applications are increasingly prevalent in radiotherapy, including commercial software solutions for automatic segmentation of anatomical structures for 3D Computed Tomography (CT). However, their use in intraoperative electron radiotherapy (IOERT) remains limited. In particular, no AI solution is available for contouring cone beam CT (CBCT) images acquired with a mobile CBCT device. The U-Net convolutional neural network architecture has gained huge success for medical image segmentation but still has difficulties capturing the global context. To increase the accu

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Sun, Mengtao, Li Lu, Ibrahim A. Hameed, Carl Petter Skaar Kulseng, and Kjell-Inge Gjesdal. "Detecting Small Anatomical Structures in 3D Knee MRI Segmentation by Fully Convolutional Networks." Applied Sciences 12, no. 1 (2021): 283. http://dx.doi.org/10.3390/app12010283.

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Accurately identifying the pixels of small organs or lesions from magnetic resonance imaging (MRI) has a critical impact on clinical diagnosis. U-net is the most well-known and commonly used neural network for image segmentation. However, the small anatomical structures in medical images cannot be well recognised by U-net. This paper explores the performance of the U-net architectures in knee MRI segmentation to find a relative structure that can obtain high accuracies for both small and large anatomical structures. To maximise the utilities of U-net architecture, we apply three types of compo

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Oliveira, Gabriel L., Claas Bollen, Wolfram Burgard, and Thomas Brox. "Efficient and robust deep networks for semantic segmentation." International Journal of Robotics Research 37, no. 4-5 (2017): 472–91. http://dx.doi.org/10.1177/0278364917710542.

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This paper explores and investigates deep convolutional neural network architectures to increase the efficiency and robustness of semantic segmentation tasks. The proposed solutions are based on up-convolutional networks. We introduce three different architectures in this work. The first architecture, called Part-Net, is designed to tackle the specific problem of human body part segmentation and to provide robustness to overfitting and body part occlusion. The second network, called Fast-Net, is a network specifically designed to provide the smallest computation load without losing representat

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Zhang, Wei, and Jian Qiu Yu. "Observation about Architecture Skin Using New Material." Advanced Materials Research 535-537 (June 2012): 1893–97. http://dx.doi.org/10.4028/www.scientific.net/amr.535-537.1893.

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This article researches the newly emerged architectural skin materials such as ETFE membranous material, paper, fabric, plastic, mental tapping board and mental weaving net, printing glass and colored glass, and various special materials. It analyses their characters and impression effects in architecture.

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Brémond Martin, Clara, Camille Simon Chane, Cédric Clouchoux, and Aymeric Histace. "Mu-Net a Light Architecture for Small Dataset Segmentation of Brain Organoid Bright-Field Images." Biomedicines 11, no. 10 (2023): 2687. http://dx.doi.org/10.3390/biomedicines11102687.

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To characterize the growth of brain organoids (BOs), cultures that replicate some early physiological or pathological developments of the human brain are usually manually extracted. Due to their novelty, only small datasets of these images are available, but segmenting the organoid shape automatically with deep learning (DL) tools requires a larger number of images. Light U-Net segmentation architectures, which reduce the training time while increasing the sensitivity under small input datasets, have recently emerged. We further reduce the U-Net architecture and compare the proposed architectu

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Men, Yifang, Yuan Yao, Miaomiao Cui, Zhouhui Lian, and Xuansong Xie. "DCT-net." ACM Transactions on Graphics 41, no. 4 (2022): 1–9. http://dx.doi.org/10.1145/3528223.3530159.

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This paper introduces DCT-Net, a novel image translation architecture for few-shot portrait stylization. Given limited style exemplars (~100), the new architecture can produce high-quality style transfer results with advanced ability to synthesize high-fidelity contents and strong generality to handle complicated scenes (e.g., occlusions and accessories). Moreover, it enables full-body image translation via one elegant evaluation network trained by partial observations (i.e., stylized heads). Few-shot learning based style transfer is challenging since the learned model can easily become overfi

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Beeche, Cameron, Jatin P. Singh, Joseph K. Leader, et al. "Super U-Net: A modularized generalizable architecture." Pattern Recognition 128 (August 2022): 108669. http://dx.doi.org/10.1016/j.patcog.2022.108669.

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Kennedy, Matthew, and Avinash Karanth Kodi. "CLAP-NET: Bandwidth adaptive optical crossbar architecture." Journal of Parallel and Distributed Computing 100 (February 2017): 130–39. http://dx.doi.org/10.1016/j.jpdc.2016.05.004.

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Jordan, Scott. "Implications of Internet architecture on net neutrality." ACM Transactions on Internet Technology 9, no. 2 (2009): 1–28. http://dx.doi.org/10.1145/1516539.1516540.

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Nelson, John A. "Radar Open System Architecture provides net centricity." IEEE Aerospace and Electronic Systems Magazine 25, no. 10 (2010): 17–20. http://dx.doi.org/10.1109/maes.2010.5631721.

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LaCerte, Yves. "4.4.2 Net-Centric Dynamic System Model Architecture." INCOSE International Symposium 14, no. 1 (2004): 774–86. http://dx.doi.org/10.1002/j.2334-5837.2004.tb00533.x.

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Pradhiba D, Kaviya K, Priyadharshini R, and Swathi R. "Ultrasound Nerve Segmentation Using RESU-NET Architecture." International Research Journal on Advanced Engineering Hub (IRJAEH) 3, no. 03 (2025): 443–47. https://doi.org/10.47392/irjaeh.2025.0061.

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Ultrasound Nerve Segmentation enhances the precision and safety of ultrasound-guided procedures by automating nerve identification using deep learning, specifically Convolutional Neural Networks (CNNs). This project employs an optimized U-Net architecture trained on labeled ultrasound datasets, with preprocessing techniques like augmentation and normalization to improve robustness. Dice Loss is used as the objective function, ensuring high segmentation accuracy, evaluated through metrics like Intersection over Union (IoU) and Dice Coefficient. Post-processing methods further refine segmentatio

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Miron, Casian, Laura Ioana Grigoras, Radu Ciucu, and Vasile Manta. "Eye Image Segmentation Method Based on the Modified U-Net CNN Architecture." Bulletin of the Polytechnic Institute of Iași. Electrical Engineering, Power Engineering, Electronics Section 67, no. 2 (2021): 41–52. http://dx.doi.org/10.2478/bipie-2021-0010.

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Abstract The paper presents a new eye image segmentation method used to extract the pupil contour based on the modified U-Net CNN architecture. The analysis was performed using two databases which contain IR images with a spatial resolution of 640x480 pixels. The first database was acquired in our laboratory and contains 400 eye images and the second database is a selection of 400 images from the publicly available CASIA Iris Lamp database. The results obtained by applying the segmentation based on the CNN architecture were compared to manually-annotated ground truth data. The results obtained

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Lutsenko, V. S., and A. E. Shukhman. "SEGMENTATION OF MEDICAL IMAGES BY CONVOLUTIONAL NEURAL NETWORKS." Vestnik komp'iuternykh i informatsionnykh tekhnologii, no. 216 (June 2022): 40–50. http://dx.doi.org/10.14489/vkit.2022.06.pp.040-050.

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Our study briefly discusses the architectures of convolutional neural networks (CNN), their advantages and disadvantages. The features of the architecture of the convolutional neural network U-net are described. An analysis of the CNN U-net was carried out, based on the analysis, a rationale was given for choosing the CNN U-net as the main architecture for using and building subsequent created and analyzed models of cert neural networks to solve the problem of segmentation of medical images. The analysis of architectures of convolutional neural networks, which can be used as convolutional laye

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Liu, Xiaoli, Ruoqi Yin, and Jianqin Yin. "Attention V-Net: A Modified V-Net Architecture for Left Atrial Segmentation." Applied Sciences 12, no. 8 (2022): 3764. http://dx.doi.org/10.3390/app12083764.

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We propose a fully convolutional neural network based on the attention mechanism for 3D medical image segmentation tasks. It can adaptively learn to highlight the salient features of images that are useful for image segmentation tasks. Some prior methods enhance accuracy using multi-scale feature fusion or dilated convolution, which is basically artificial and lacks the flexibility of the model itself. Therefore, some works proposed the 2D attention gate module, but these works process 2D medical slice images, ignoring the correlation between 3D image sequences. In contrast, the 3D attention g

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Yousef, Rammah, Shakir Khan, Gaurav Gupta, et al. "U-Net-Based Models towards Optimal MR Brain Image Segmentation." Diagnostics 13, no. 9 (2023): 1624. http://dx.doi.org/10.3390/diagnostics13091624.

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Brain tumor segmentation from MRIs has always been a challenging task for radiologists, therefore, an automatic and generalized system to address this task is needed. Among all other deep learning techniques used in medical imaging, U-Net-based variants are the most used models found in the literature to segment medical images with respect to different modalities. Therefore, the goal of this paper is to examine the numerous advancements and innovations in the U-Net architecture, as well as recent trends, with the aim of highlighting the ongoing potential of U-Net being used to better the perfo

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von Eicken, T., A. Basu, V. Buch, and W. Vogels. "U-Net." ACM SIGOPS Operating Systems Review 29, no. 5 (1995): 40–53. http://dx.doi.org/10.1145/224057.224061.

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Suprihatin, Bambang, Yuli Andriani, Fauziah Nuraini Kurdi, Anita Desiani, Ibra Giovani Dwi Putra, and Muhammad Akmal Shidqi. "Lungs X-Ray Image Segmentation and Classification of Lung Disease using Convolutional Neural Network Architectures." MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer 23, no. 1 (2023): 67–78. http://dx.doi.org/10.30812/matrik.v23i1.3133.

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Lung disease is one of the biggest causes of death in the world. The SARS-CoV-2 virus causes diseases like COVID-19, and the bacteria Streptococcus sp., which causes pneumonia, are two sample causes of lung disease. X-ray images are used to detect the lung disease. This study aimed to combine the stages of segmentation and classification of lung disease. This study in segmentation aims to separate the features contained in the lung images. The classification aimed to provide holistic information on lung disease. This research method used the Deep Residual U-Net (DrU-Net) segmentation architect

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Cho, Hyeonjeong, Jae Sung Lee, Jin Sung Kim, Woong Sub Koom, and Hojin Kim. "Empowering Vision Transformer by Network Hyper-Parameter Selection for Whole Pelvis Prostate Planning Target Volume Auto-Segmentation." Cancers 15, no. 23 (2023): 5507. http://dx.doi.org/10.3390/cancers15235507.

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U-Net, based on a deep convolutional network (CNN), has been clinically used to auto-segment normal organs, while still being limited to the planning target volume (PTV) segmentation. This work aims to address the problems in two aspects: 1) apply one of the newest network architectures such as vision transformers other than the CNN-based networks, and 2) find an appropriate combination of network hyper-parameters with reference to recently proposed nnU-Net (“no-new-Net”). VT U-Net was adopted for auto-segmenting the whole pelvis prostate PTV as it consisted of fully transformer architecture.

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Dormagen, Niklas, Max Klein, Andreas S. Schmitz, Markus H. Thoma, and Mike Schwarz. "Multi-Particle Tracking in Complex Plasmas Using a Simplified and Compact U-Net." Journal of Imaging 10, no. 2 (2024): 40. http://dx.doi.org/10.3390/jimaging10020040.

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Detecting micron-sized particles is an essential task for the analysis of complex plasmas because a large part of the analysis is based on the initially detected positions of the particles. Accordingly, high accuracy in particle detection is desirable. Previous studies have shown that machine learning algorithms have made great progress and outperformed classical approaches. This work presents an approach for tracking micron-sized particles in a dense cloud of particles in a dusty plasma at Plasmakristall-Experiment 4 using a U-Net. The U-net is a convolutional network architecture for the fas

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Ramadhani, Syafira Dian, Erwin Erwin, Anita Desiani, and Sinta Bella Agustina. "BLOOD VESSEL SEGMENTATION IN RETINAL IMAGES USING RESVNET ARCHITECTURE." Jurnal Teknik Informatika (Jutif) 5, no. 4 (2024): 1139–47. https://doi.org/10.52436/1.jutif.2024.5.4.2637.

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The U-Net architecture is often used in medical blood vessel segmentation due to its ability to produce good segmentation. However, U-Net has high complexity due to the presence of the bridge part, which increases the parameters and training time. To overcome this, this research modifies U-Net by removing the bridge part, resulting in V-Net architecture. V-Net architecture faces challenges in capturing deep and complex features. This research proposes modifying V-Net with ResNet architecture in the encoder part, resulting in ResVNet architecture. ResNet, with residual connections, enables the

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S. Mahmmed, Basmal, and Ammar I. Majeed. "FACE DETECTION AND RECOGNITION USING GOOGLE-NET ARCHITECTURE." Iraqi Journal of Information and Communication Technology 6, no. 1 (2023): 66–79. http://dx.doi.org/10.31987/ijict.6.1.228.

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Using face detection to secure places is an important application merging with machine vision. This paper reposed a system to do face detection and recognition using existing architecture google-net and transfer learning to let the network learn images based on pre-trained architecture, The design of a network leads to an architecture that leads to maximizing the accuracy of the system and accurately detecting faces that are saved to the database and specifying the effect of weights used within the nodes of the hidden layer which consider the most time-consuming task within the architecture. T

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D. S., Chandra Sekaran, and Christopher Clement J. "G-Net: Implementing an enhanced brain tumor segmentation framework using semantic segmentation design." PLOS ONE 19, no. 8 (2024): e0308236. http://dx.doi.org/10.1371/journal.pone.0308236.

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A fundamental computer vision task called semantic segmentation has significant uses in the understanding of medical pictures, including the segmentation of tumors in the brain. The G-Shaped Net architecture appears in this context as an innovative and promising design that combines components from many models to attain improved accuracy and efficiency. In order to improve efficiency, the G-Shaped Net architecture synergistically incorporates four fundamental components: the Self-Attention, Squeeze Excitation, Fusion, and Spatial Pyramid Pooling block structures. These factors work together to

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Jessica, A. Sciammarelli. "3D U-NET FOR BRAIN TUMOUR SEGMENTATION." Revistaft 27, no. 121 (2023): 89. https://doi.org/10.5281/zenodo.7878789.

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The brain is the most complex part of the human body that controls memory, emotions,  touch, motor, thought, skills, vision, breathing, temperature and every process related to  regulating the human body. Usually, the brain tumour can be classified as malignant or  benign, and it can spread to other regions as sometimes not. (MRI) Magnetic Resonance  Imaging is the most common exam to identify the tumours, and later the resection surgery as  a decision that has to be made from neurosurgeon. The specialized doctor has to mark the  tumour region precisely and a manu

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Wu, Shuqiong, Megumi Nakao, Keiho Imanishi, Mitsuhiro Nakamura, Takashi Mizowaki, and Tetsuya Matsuda. "Computed Tomography slice interpolation in the longitudinal direction based on deep learning techniques: To reduce slice thickness or slice increment without dose increase." PLOS ONE 17, no. 12 (2022): e0279005. http://dx.doi.org/10.1371/journal.pone.0279005.

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Large slice thickness or slice increment causes information insufficiency of Computed Tomography (CT) data in the longitudinal direction, which degrades the quality of CT-based diagnosis. Traditional approaches such as high-resolution computed tomography (HRCT) and linear interpolation can solve this problem. However, HRCT suffers from dose increase, and linear interpolation causes artifacts. In this study, we propose a deep-learning-based approach to reconstruct densely sliced CT from sparsely sliced CT data without any dose increase. The proposed method reconstructs CT images from neighborin

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Gao, Yachun, Jia Guo, Chuanji Fu, Yan Wang, and Shimin Cai. "VLSM-Net: A Fusion Architecture for CT Image Segmentation." Applied Sciences 13, no. 7 (2023): 4384. http://dx.doi.org/10.3390/app13074384.

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Region of interest (ROI) segmentation is a key step in computer-aided diagnosis (CAD). With the problems of blurred tissue edges and imprecise boundaries of ROI in medical images, it is hard to extract satisfactory ROIs from medical images. In order to overcome the shortcomings in segmentation from the V-Net model or the level set method (LSM), we propose in this paper a new image segmentation method, the VLSM-Net model, combining these two methods. Specifically, we first use the V-Net model to segment the ROIs, and set the segmentation result as the initial contour. It is then fed through the

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Guleria, Pratiyush. "Data Access Layer: A Programming Paradigm on Cloud." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 11, no. 3 (2013): 2341–45. http://dx.doi.org/10.24297/ijct.v11i3.1164.

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Database is important for any application and critical part of private and public cloud platforms. For compatibility with cloud computing we can follow architectures like three tier architecture in .Net Technologies such that database layer should be separate from user and business logic layers. There are some other issues like following ACID properties in databases, providing dynamic scalability by using Shared-disk Architecture and efficient multi-tenancy, elastic scalability, and database privacy.

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