Log in

Relevant bibliographies by topics / Deep learning segmentation / Journal articles

To see the other types of publications on this topic, follow the link: Deep learning segmentation.

Journal articles on the topic 'Deep learning segmentation'

Author: Grafiati

Published: 19 April 2023

Last updated: 25 July 2025

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the top 50 journal articles for your research on the topic 'Deep learning segmentation.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.

1

Dadras, Armin A., and Philipp Aichinger. "Deep Learning-Based Detection of Glottis Segmentation Failures." Bioengineering 11, no. 5 (2024): 443. http://dx.doi.org/10.3390/bioengineering11050443.

Full text

Abstract:

Medical image segmentation is crucial for clinical applications, but challenges persist due to noise and variability. In particular, accurate glottis segmentation from high-speed videos is vital for voice research and diagnostics. Manual searching for failed segmentations is labor-intensive, prompting interest in automated methods. This paper proposes the first deep learning approach for detecting faulty glottis segmentations. For this purpose, faulty segmentations are generated by applying both a poorly performing neural network and perturbation procedures to three public datasets. Heavy data

APA, Harvard, Vancouver, ISO, and other styles

2

Ahmed Ali, Salim, and B. G Prasad. "Semantic Segmentation using Deep Learning Approaches - A Study." International Journal of Science and Research (IJSR) 10, no. 7 (2021): 113–16. https://doi.org/10.21275/sr21630194436.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

Noori, Amani Y., Dr Shaimaa H. Shaker, and Dr Raghad Abdulaali Azeez. "Semantic Segmentation of Urban Street Scenes Using Deep Learning." Webology 19, no. 1 (2022): 2294–306. http://dx.doi.org/10.14704/web/v19i1/web19156.

Full text

Abstract:

Scene classification is essential conception task used by robotics for understanding the environmental. The outdoor scene like urban street scene is composing of image with depth having greater variety than iconic object image. The semantic segmentation is an important task for autonomous driving and mobile robotics applications because it introduces enormous information need for safe navigation and complex reasoning. This paper introduces a model for classification all pixel’s image and predicates the right object that contains this pixel. This model adapts famous network image classification

APA, Harvard, Vancouver, ISO, and other styles

4

Weishaupt, L. L., T. Vuong, A. Thibodeau-Antonacci, et al. "A121 QUANTIFYING INTER-OBSERVER VARIABILITY IN THE SEGMENTATION OF RECTAL TUMORS IN ENDOSCOPY IMAGES AND ITS EFFECTS ON DEEP LEARNING." Journal of the Canadian Association of Gastroenterology 5, Supplement_1 (2022): 140–42. http://dx.doi.org/10.1093/jcag/gwab049.120.

Full text

Abstract:

Abstract Background Tumor delineation in endoscopy images is a crucial part of clinical diagnoses and treatment planning for rectal cancer patients. However, it is challenging to detect and adequately determine the size of tumors in these images, especially for inexperienced clinicians. This motivates the need for a standardized, automated segmentation method. While deep learning has proven to be a powerful tool for medical image segmentation, it requires a large quantity of high-quality annotated training data. Since the annotation of endoscopy images is prone to high inter-observer variabili

APA, Harvard, Vancouver, ISO, and other styles

5

AL-Oudat, Mohammad, Mohammad Azzeh, Hazem Qattous, Ahmad Altamimi, and Saleh Alomari. "Image Segmentation based Deep Learning for Biliary Tree Diagnosis." Webology 19, no. 1 (2022): 1834–49. http://dx.doi.org/10.14704/web/v19i1/web19123.

Full text

Abstract:

Dilation of biliary tree can be an indicator of several diseases such as stones, tumors, benign strictures, and some cases cancer. This dilation can be due to many reasons such as gallstones, inflammation of the bile ducts, trauma, injury, severe liver damage. Automatic measurement of the biliary tree in magnetic resonance images (MRI) is helpful to assist hepatobiliary surgeons for minimally invasive surgery. In this paper, we proposed a model to segment biliary tree MRI images using a Fully Convolutional Neural (FCN) network. Based on the extracted area, seven features that include Entropy,

APA, Harvard, Vancouver, ISO, and other styles

6

Mrinalini, kakroo, and mansotra Vibhakar. "Automatic Segmentation of liver Tumor using Deep Learning." Journal of Scientific Research and Technology (JSRT) 1, no. 5 (2023): 100–113. https://doi.org/10.5281/zenodo.8276825.

Full text

Abstract:

When it comes to medical imaging data like CT or MRI images, automatic segmentation of liver tumors is the process of precisely locating and isolating tumor spots without the need for human involvement. Liver tumor segmentation is crucial for accurate diagnosis and therapeutic planning of liver cancer. The purpose of this work is to provide a comprehensive summary of the state-of-the-art approaches to automatically segmenting liver cancers from medical imaging data. Here, we'll go through some of the more general and specialized approaches now in use in this field. By allowing for more pre

APA, Harvard, Vancouver, ISO, and other styles

7

Ng, Hwee Ping, Qijian Chan, Zheng Jie Tan, Ronnie Ssebaggala, and Joseph John Lifton. "Segmenting spatter particles on additively manufactured surfaces using deep learning." Surface Topography: Metrology and Properties 13, no. 1 (2025): 015006. https://doi.org/10.1088/2051-672x/ada6e1.

Full text

Abstract:

Abstract Metal additively manufactured (AM) surfaces do not exhibit the same surface features as machined surfaces. Rather than cutting marks, the additive surface may display surface features such as spatter particles, weld tracks, cracks, and surface breaking pores. These features are not well described by surface height parameters that were developed for machined surfaces. Therefore, an AM specific surface characterisation approach is required; feature based surface characterisation is a promising approach, but it requires surface features to be manually segmented which is a subjective proc

APA, Harvard, Vancouver, ISO, and other styles

8

Sri, S. Vinitha, and S. P. Kavya. "Lung Segmentation Using Deep Learning." Asian Journal of Applied Science and Technology 05, no. 02 (2021): 10–19. http://dx.doi.org/10.38177/ajast.2021.5202.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

Vogt, Nina. "Neuron segmentation with deep learning." Nature Methods 16, no. 6 (2019): 460. http://dx.doi.org/10.1038/s41592-019-0450-7.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

Al-Behadili, Husam, Omar A. Athab, and Saddam K. Alwane. "Deep Learning Based Teeth Segmentation." Revue d'Intelligence Artificielle 38, no. 4 (2024): 1173–81. http://dx.doi.org/10.18280/ria.380411.

Full text

APA, Harvard, Vancouver, ISO, and other styles

11

Chigurupati, Amarnath. "Deep Learning for Liver Segmentation." International Journal of Scientific Research and Engineering Trends 11, no. 2 (2025): 1955–59. https://doi.org/10.61137/ijsret.vol.11.issue2.375.

Full text

APA, Harvard, Vancouver, ISO, and other styles

12

Anshdeep, Singh Kapula, Aishwarya Esha, Pranav Shiva, Jaggi Jattin, and A. Suresh. "Waste Segmentation using Deep Learning." Waste Segmentation using Deep Learning 8, no. 11 (2023): 6. https://doi.org/10.5281/zenodo.10243786.

Full text

Abstract:

The inefficiencies in recycling bin management have far-reaching consequences, primarily manifesting as resource wastage and a deficiency in incident detection. Traditional recycling methods often fall short in accurately separating and collecting recyclable materials, resulting in valuable resources ending up in landfills or being mishandled. However, the success of recycling initiatives doesn't rely solely on technological advancements. User education is a vital component in the quest to enhance global recycling rates. Raising public awareness about the importance of recycling, the proper so

APA, Harvard, Vancouver, ISO, and other styles

13

Qayyum, Abdul, Mohamed Khan Afthab Ahamed Khan, Rana Umar Mukhtar, et al. "Automatic segmentation of intracranial hemorrhage using coarse and fine deep learning models." Imaging and Radiation Research 6, no. 2 (2023): 3088. http://dx.doi.org/10.24294/irr.v6i2.3088.

Full text

Abstract:

To save patients’ lives, it is important to go for an early diagnosis of intracranial hemorrhage (ICH). For diagnosing ICH, the widely used method is non-contrast computed tomography (NCCT). It has fast acquisition and availability in medical emergency facilities. To predict hematoma progression and mortality, it is important to estimate the volume of intracranial hemorrhage. Radiologists can manually delineate the ICH region to estimate the hematoma volume. This process takes time and undergoes inter-rater variability. In this research paper, we develop and discuss a fine segmentation model a

APA, Harvard, Vancouver, ISO, and other styles

14

Qayyum, Abdul, Mohamed Khan Afthab Ahamed Khan, Rana Umar Mukhtar, et al. "Automatic segmentation of intracranial hemorrhage using coarse and fine deep learning models." Imaging and Radiation Research 6, no. 1 (2023): 3088. http://dx.doi.org/10.24294/irr.v6i1.3088.

Full text

Abstract:

To save patients’ lives, it is important to go for an early diagnosis of intracranial hemorrhage (ICH). For diagnosing ICH, the widely used method is non-contrast computed tomography (NCCT). It has fast acquisition and availability in medical emergency facilities. To predict hematoma progression and mortality, it is important to estimate the volume of intracranial hemorrhage. Radiologists can manually delineate the ICH region to estimate the hematoma volume. This process takes time and undergoes inter-rater variability. In this research paper, we develop and discuss a fine segmentation model a

APA, Harvard, Vancouver, ISO, and other styles

15

Ding, Ruiyao. "Segmentation analysis of UAV images based on Unet deep learning algorithm." Applied and Computational Engineering 54, no. 1 (2024): 248–53. http://dx.doi.org/10.54254/2755-2721/54/20241644.

Full text

Abstract:

The continuous development of UAV technology provides us with more and higher quality data, in which the application of UAV image segmentation technology can help us better understand and process these data. Traditional image segmentation methods can no longer meet the needs of UAV image segmentation, so researchers have begun to explore the application of deep learning methods in UAV image segmentation.U-Net, as a classical deep learning model, is also widely used in UAV image segmentation.U-Net is characterized by two parts: encoder and decoder, which are used to extract the image features,

APA, Harvard, Vancouver, ISO, and other styles

16

Hyun-Cheol Park, Hyun-Cheol Park, Raman Ghimire Hyun-Cheol Park, Sahadev Poudel Raman Ghimire, and Sang-Woong Lee Sahadev Poudel. "Deep Learning for Joint Classification and Segmentation of Histopathology Image." 網際網路技術學刊 23, no. 4 (2022): 903–10. http://dx.doi.org/10.53106/160792642022072304025.

Full text

Abstract:

<p>Liver cancer is one of the most prevalent cancer deaths worldwide. Thus, early detection and diagnosis of possible liver cancer help in reducing cancer death. Histopathological Image Analysis (HIA) used to be carried out traditionally, but these are time-consuming and require expert knowledge. We propose a patch-based deep learning method for liver cell classification and segmentation. In this work, a two-step approach for the classification and segmentation of whole-slide image (WSI) is proposed. Since WSIs are too large to be fed into convolutional neural networks (CNN) directly, we

APA, Harvard, Vancouver, ISO, and other styles

17

Yang, Zi, Mingli Chen, Mahdieh Kazemimoghadam, et al. "Deep-learning and radiomics ensemble classifier for false positive reduction in brain metastases segmentation." Physics in Medicine & Biology 67, no. 2 (2022): 025004. http://dx.doi.org/10.1088/1361-6560/ac4667.

Full text

Abstract:

Abstract Stereotactic radiosurgery (SRS) is now the standard of care for brain metastases (BMs) patients. The SRS treatment planning process requires precise target delineation, which in clinical workflow for patients with multiple (>4) BMs (mBMs) could become a pronounced time bottleneck. Our group has developed an automated BMs segmentation platform to assist in this process. The accuracy of the auto-segmentation, however, is influenced by the presence of false-positive segmentations, mainly caused by the injected contrast during MRI acquisition. To address this problem and further improv

APA, Harvard, Vancouver, ISO, and other styles

18

Xue, Jie, Bao Wang, Yang Ming, et al. "Deep learning–based detection and segmentation-assisted management of brain metastases." Neuro-Oncology 22, no. 4 (2019): 505–14. http://dx.doi.org/10.1093/neuonc/noz234.

Full text

Abstract:

Abstract Background Three-dimensional T1 magnetization prepared rapid acquisition gradient echo (3D-T1-MPRAGE) is preferred in detecting brain metastases (BM) among MRI. We developed an automatic deep learning–based detection and segmentation method for BM (named BMDS net) on 3D-T1-MPRAGE images and evaluated its performance. Methods The BMDS net is a cascaded 3D fully convolution network (FCN) to automatically detect and segment BM. In total, 1652 patients with 3D-T1-MPRAGE images from 3 hospitals (n = 1201, 231, and 220, respectively) were retrospectively included. Manual segmentations were

APA, Harvard, Vancouver, ISO, and other styles

19

S, Arivazhagan, Arun M, Ruby Ranjitha Mary F, and Shamyughtha Bala B. "CAPTCHA RECOGNITION USING MACHINE LEARNING AND DEEP LEARNING TECHNIQUES." ICTACT Journal on Data Science and Machine Learning 5, no. 3 (2024): 641–49. https://doi.org/10.21917/ijdsml.2024.0135.

Full text

Abstract:

CAPTCHAs are widely used on the internet to determine whether a user is a human, and text-based CAPTCHAs are mostly used. The study on CAPTCHA recognition is meant for detecting the vulnerabilities in their security for preventing any malicious intrusion in the network. In this article, the Segmentation-based method and Segmentation-free method are used for recognition. In segmentation-based technique, text CAPTCHAs are segmented using contours and bounding-box method, SIFT and KAZE features are extracted and Support Vector Machine (SVM) and modified LeNet-5 model is used for recognition. In S

APA, Harvard, Vancouver, ISO, and other styles

20

Iwaszenko, Sebastian, and Leokadia Róg. "Application of Deep Learning in Petrographic Coal Images Segmentation." Minerals 11, no. 11 (2021): 1265. http://dx.doi.org/10.3390/min11111265.

Full text

Abstract:

The study of the petrographic structure of medium- and high-rank coals is important from both a cognitive and a utilitarian point of view. The petrographic constituents and their individual characteristics and features are responsible for the properties of coal and the way it behaves in various technological processes. This paper considers the application of convolutional neural networks for coal petrographic images segmentation. The U-Net-based model for segmentation was proposed. The network was trained to segment inertinite, liptinite, and vitrinite. The segmentations prepared manually by a

APA, Harvard, Vancouver, ISO, and other styles

21

Napte, Kiran, and Anurag Mahajan. "Deep Learning based Liver Segmentation: A Review." Revue d'Intelligence Artificielle 36, no. 6 (2022): 979–84. http://dx.doi.org/10.18280/ria.360620.

Full text

Abstract:

Tremendous advancement takes place in the field of medical science. With this advancement, it is possible to support diagnosis and treatment planning for various diseases related to the abdominal organ. The liver is one of the adnominal organs, a common site for developing tumors. Liver disease is one of the main causes of death. Due to its complex and heterogeneous nature and shape, it is challenging to segment the liver and its tumor. There are numerous methods available for liver segmentation. Some are handcrafted, semi-automatic, and fully automatic. Image segmentation using deep learning

APA, Harvard, Vancouver, ISO, and other styles

22

Huang, Keke. "Application of deep learning in medical imaging segmentation." Theoretical and Natural Science 17, no. 1 (2023): 19–25. http://dx.doi.org/10.54254/2753-8818/17/20240702.

Full text

Abstract:

The increasing demand for segmentation of lesions in medical images necessitates research on automatic segmentation. Manual segmentation is inefficient due to training time and energy constraints. Deep learning-based image segmentation technology can improve efficiency and aid in diagnosing conditions. This technology provides accurate and detailed data support for clinical medicine, making it a crucial field in medical image processing. This essay introduces image segmentation and its classification, and explains the differences between two segmentation methods, semantic segmentation and inst

APA, Harvard, Vancouver, ISO, and other styles

23

Buser, Myrthe A. D., Alida F. W. van der Steeg, Marc H. W. A. Wijnen, et al. "Radiologic versus Segmentation Measurements to Quantify Wilms Tumor Volume on MRI in Pediatric Patients." Cancers 15, no. 7 (2023): 2115. http://dx.doi.org/10.3390/cancers15072115.

Full text

Abstract:

Wilms tumor is a common pediatric solid tumor. To evaluate tumor response to chemotherapy and decide whether nephron-sparing surgery is possible, tumor volume measurements based on magnetic resonance imaging (MRI) are important. Currently, radiological volume measurements are based on measuring tumor dimensions in three directions. Manual segmentation-based volume measurements might be more accurate, but this process is time-consuming and user-dependent. The aim of this study was to investigate whether manual segmentation-based volume measurements are more accurate and to explore whether these

APA, Harvard, Vancouver, ISO, and other styles

24

Dhivya, P., S. Vanithamani, M. Malini, and T. Kanimozhi. "Adaptive Edge-Guided Segmentation with Biftransnet for Gastrointestinal Tract Image Segmentation." Indian Journal Of Science And Technology 17, no. 43 (2024): 4524–35. http://dx.doi.org/10.17485/ijst/v17i43.3504.

Full text

Abstract:

Objectives: The primary goal of this research is to improve the segmentation accuracy of Gastrointestinal (GI) tract images, addressing the challenges posed by complex anatomical structures, noise artifacts, and varying imaging modalities. The study aims to overcome the limitations of traditional techniques like thresholding and region growing, as well as mitigate the drawbacks of current deep learning-based models, such as high computational costs and overfitting. Methods: UW-Madison GI Tract Images are collected from the Kaggle repository. the proposed method introduces an Adaptive Edge-Guid

APA, Harvard, Vancouver, ISO, and other styles

25

Moorthy, Jayashree, and Usha Devi Gandhi. "A Survey on Medical Image Segmentation Based on Deep Learning Techniques." Big Data and Cognitive Computing 6, no. 4 (2022): 117. http://dx.doi.org/10.3390/bdcc6040117.

Full text

Abstract:

Deep learning techniques have rapidly become important as a preferred method for evaluating medical image segmentation. This survey analyses different contributions in the deep learning medical field, including the major common issues published in recent years, and also discusses the fundamentals of deep learning concepts applicable to medical image segmentation. The study of deep learning can be applied to image categorization, object recognition, segmentation, registration, and other tasks. First, the basic ideas of deep learning techniques, applications, and frameworks are introduced. Deep

APA, Harvard, Vancouver, ISO, and other styles

26

Iyer, Aditi, Maria Thor, Ifeanyirochukwu Onochie, et al. "Prospectively-validated deep learning model for segmenting swallowing and chewing structures in CT." Physics in Medicine & Biology 67, no. 2 (2022): 024001. http://dx.doi.org/10.1088/1361-6560/ac4000.

Full text

Abstract:

Abstract Objective. Delineating swallowing and chewing structures aids in radiotherapy (RT) treatment planning to limit dysphagia, trismus, and speech dysfunction. We aim to develop an accurate and efficient method to automate this process. Approach. CT scans of 242 head and neck (H&N) cancer patients acquired from 2004 to 2009 at our institution were used to develop auto-segmentation models for the masseters, medial pterygoids, larynx, and pharyngeal constrictor muscle using DeepLabV3+. A cascaded framework was used, wherein models were trained sequentially to spatially constrain each str

APA, Harvard, Vancouver, ISO, and other styles

27

You, Siming. "Deep learning in autonomous driving: Advantages, limitations, and innovative solutions." Applied and Computational Engineering 75, no. 1 (2024): 147–53. http://dx.doi.org/10.54254/2755-2721/75/20240528.

Full text

Abstract:

With the rapid development of autonomous driving technology, deep learning has become a core driver for innovation in testing autonomous driving scenarios. This review paper delves into the critical role of deep learning in autonomous driving technology. The paper will describe how deep learning is at the center of driving innovation. The paper thoroughly explores the application of deep learning in obstacle detection, scene classification and understanding, and image segmentation, emphasizing the significant benefits in perception and decision-making while pointing out the challenges and inno

APA, Harvard, Vancouver, ISO, and other styles

28

Hu, Guangdong, Fengyuan Qian, Longgui Sha, and Zilong Wei. "Application of Deep Learning Technology in Glioma." Journal of Healthcare Engineering 2022 (February 18, 2022): 1–9. http://dx.doi.org/10.1155/2022/8507773.

Full text

Abstract:

A common and most basic brain tumor is glioma that is exceptionally dangerous to health of various patients. A glioma segmentation, which is primarily magnetic resonance imaging (MRI) oriented, is considered as one of common tools developed for doctors. These doctors use this system to examine, analyse, and diagnose appearance of the glioma’s outward for both patients, i.e., indoor and outdoor. In the literature, a widely utilized approach for the segmentation of glioma is the deep learning-oriented method. To cope with this issue, a segmentation of glioma approach, i.e., primarily on the conv

APA, Harvard, Vancouver, ISO, and other styles

29

Hess, Hanspeter, Adrian C. Ruckli, Finn Bürki, et al. "Deep-Learning-Based Segmentation of the Shoulder from MRI with Inference Accuracy Prediction." Diagnostics 13, no. 10 (2023): 1668. http://dx.doi.org/10.3390/diagnostics13101668.

Full text

Abstract:

Three-dimensional (3D)-image-based anatomical analysis of rotator cuff tear patients has been proposed as a way to improve repair prognosis analysis to reduce the incidence of postoperative retear. However, for application in clinics, an efficient and robust method for the segmentation of anatomy from MRI is required. We present the use of a deep learning network for automatic segmentation of the humerus, scapula, and rotator cuff muscles with integrated automatic result verification. Trained on N = 111 and tested on N = 60 diagnostic T1-weighted MRI of 76 rotator cuff tear patients acquired f

APA, Harvard, Vancouver, ISO, and other styles

30

Hussain, Dildar, Rizwan Ali Naqvi, Woong-Kee Loh, and Jooyoung Lee. "Deep Learning in DXA Image Segmentation." Computers, Materials & Continua 66, no. 3 (2021): 2587–98. http://dx.doi.org/10.32604/cmc.2021.013031.

Full text

APA, Harvard, Vancouver, ISO, and other styles

31

Akiyama, T. S., J. Marcato Junior, W. N. Gonçalves, et al. "DEEP LEARNING APPLIED TO WATER SEGMENTATION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B2-2020 (August 14, 2020): 1189–93. http://dx.doi.org/10.5194/isprs-archives-xliii-b2-2020-1189-2020.

Full text

Abstract:

Abstract. The use of deep learning (DL) with convolutional neural networks (CNN) to monitor surface water can be a valuable supplement to costly and labour-intense standard gauging stations. This paper presents the application of a recent CNN semantic segmentation method (SegNet) to automatically segment river water in imagery acquired by RGB sensors. This approach can be used as a new supporting tool because there are only a few studies using DL techniques to monitor water resources. The study area is a medium-scale river (Wesenitz) located in the East of Germany. The captured images reflect

APA, Harvard, Vancouver, ISO, and other styles

32

Benbrahim Ansari, Oussama. "Geo-Marketing Segmentation with Deep Learning." Businesses 1, no. 1 (2021): 51–71. http://dx.doi.org/10.3390/businesses1010005.

Full text

Abstract:

Spatial clustering is a fundamental instrument in modern geo-marketing. The complexity of handling of high-dimensional and geo-referenced data in the context of distribution networks imposes important challenges for marketers to catch the right customer segments with useful pattern similarities. The increasing availability of the geo-referenced data also places more pressure on the existing geo-marketing methods and makes it more difficult to detect hidden or non-linear relationships between the variables. In recent years, artificial neural networks have been established in different disciplin

APA, Harvard, Vancouver, ISO, and other styles

33

Junior, Gerivan Santos, Janderson Ferreira, Cristian Millán-Arias, Ramiro Daniel, Alberto Casado Junior, and Bruno J. T. Fernandes. "Ceramic Cracks Segmentation with Deep Learning." Applied Sciences 11, no. 13 (2021): 6017. http://dx.doi.org/10.3390/app11136017.

Full text

Abstract:

Cracks are pathologies whose appearance in ceramic tiles can cause various damages due to the coating system losing water tightness and impermeability functions. Besides, the detachment of a ceramic plate, exposing the building structure, can still reach people who move around the building. Manual inspection is the most common method for addressing this problem. However, it depends on the knowledge and experience of those who perform the analysis and demands a long time and a high cost to map the entire area. This work focuses on automated optical inspection to find faults in ceramic tiles per

APA, Harvard, Vancouver, ISO, and other styles

34

V., Pattabiraman, and Harshit Singh. "Deep Learning based Brain Tumour Segmentation." WSEAS TRANSACTIONS ON COMPUTERS 19 (January 4, 2021): 234–41. http://dx.doi.org/10.37394/23205.2020.19.29.

Full text

Abstract:

Artificial Intelligence has changed our outlook towards the whole world and it is regularly used to better understand all the data and information that surrounds us in our everyday lives. One such application of Artificial Intelligence in real world scenarios is extraction of data from various images and interpreting it in different ways. This includes applications like object detection, image segmentation, image restoration, etc. While every technique has its own area of application image segmentation has a variety of applications extending from complex medical field to regular pattern identi

APA, Harvard, Vancouver, ISO, and other styles

35

Kurama, Vihar, Samhita Alla, and Rohith Vishnu K. "Image Semantic Segmentation Using Deep Learning." International Journal of Image, Graphics and Signal Processing 10, no. 12 (2018): 1–10. http://dx.doi.org/10.5815/ijigsp.2018.12.01.

Full text

APA, Harvard, Vancouver, ISO, and other styles

36

Sardar, Mousumi, Subhashis Banerjee, and Sushmita Mitra. "Iris Segmentation Using Interactive Deep Learning." IEEE Access 8 (2020): 219322–30. http://dx.doi.org/10.1109/access.2020.3041519.

Full text

APA, Harvard, Vancouver, ISO, and other styles

37

Pandian, Asha, Bala Gopi Sai Kumar, Venkata Revanth, and Kanipakam Sai Kiran. "Brain Tumor Segmentation Using Deep Learning." Journal of Computational and Theoretical Nanoscience 17, no. 8 (2020): 3648–52. http://dx.doi.org/10.1166/jctn.2020.9247.

Full text

Abstract:

Manual recognition of the cerebrum tumor for malignant growth determination from MRI pictures is a troublesome, repetitive and tedious assignment. The precision and the power of cerebrum Tumor discovery in this way, are significant for the determination, treatment arranging, and treatment result assessment. Generally, the programmed cerebrum tumor location techniques use hand structured highlights. Correspondingly, customary strategies for profound learning, for example, ordinary neural systems require a lot of commented on information to learn from, which is frequently hard to acquire in clin

APA, Harvard, Vancouver, ISO, and other styles

38

Zhang, Xiaoqing. "Melanoma segmentation based on deep learning." Computer Assisted Surgery 22, sup1 (2017): 267–77. http://dx.doi.org/10.1080/24699322.2017.1389405.

Full text

APA, Harvard, Vancouver, ISO, and other styles

39

Singh, Abhineet, Hayden Kalke, Mark Loewen, and Nilanjan Ray. "River Ice Segmentation With Deep Learning." IEEE Transactions on Geoscience and Remote Sensing 58, no. 11 (2020): 7570–79. http://dx.doi.org/10.1109/tgrs.2020.2981082.

Full text

APA, Harvard, Vancouver, ISO, and other styles

40

Pekala, M., N. Joshi, T. Y. Alvin Liu, N. M. Bressler, D. Cabrera DeBuc, and P. Burlina. "Deep learning based retinal OCT segmentation." Computers in Biology and Medicine 114 (November 2019): 103445. http://dx.doi.org/10.1016/j.compbiomed.2019.103445.

Full text

APA, Harvard, Vancouver, ISO, and other styles

41

Krithika, S. "Brain Tumor Segmentation Using Deep Learning." International Journal for Research in Applied Science and Engineering Technology 11, no. 7 (2023): 171–74. http://dx.doi.org/10.22214/ijraset.2023.54585.

Full text

Abstract:

Abstract: Now a day’s tumor is second leading cause of cancer. Due to cancer large no of patients are in danger. The medical field needs fast, automated, efficient, and reliable technique to detect tumor like brain tumor. Detection plays very important role in treatment. If proper detection of tumor is possible then doctors keep a patient out of danger. Various image processing techniques are used in this application. Using this application doctors provide proper treatment and save tumor patients. A tumor is nothing but excess cells growing in an uncontrolled manner. Brain tumor cells grow in

APA, Harvard, Vancouver, ISO, and other styles

42

Bouzaachane, K., A. Darouichi, and E. El Guarmah. "Deep learning for photovoltaic panels segmentation." Mathematical Modeling and Computing 10, no. 3 (2023): 638–50. http://dx.doi.org/10.23939/mmc2023.03.638.

Full text

Abstract:

Due to advanced sensor technology, satellites and unmanned aerial vehicles (UAV) are producing a huge amount of data allowing advancement in all different kinds of earth observation applications. Thanks to this source of information, and driven by climate change concerns, renewable energy assessment became an increasing necessity among researchers and companies. Solar power, going from household rooftops to utility-scale farms, is reshaping the energy markets around the globe. However, the automatic identification of photovoltaic (PV) panels and solar farms' status is still an open question th

APA, Harvard, Vancouver, ISO, and other styles

43

Liveraro, Gianni Shigeru Setoue, Maria Emília Seren Takahashi, Fabiana Lascala, Maria Carolina Santos Mendes, Jun Takahashi, and José Barreto Campello Carvalheira. "DEEP LEARNING FOR CT IMAGES SEGMENTATION." Hematology, Transfusion and Cell Therapy 46 (April 2024): S6. http://dx.doi.org/10.1016/j.htct.2024.04.058.

Full text

APA, Harvard, Vancouver, ISO, and other styles

44

Sajid, Muhammad, Wajeeha Yaseen, and Aman Ullah Khan. "Brain Tumor Segmentation using Deep Learning." VFAST Transactions on Software Engineering 11, no. 2 (2023): 113–23. http://dx.doi.org/10.21015/vtse.v11i2.1533.

Full text

Abstract:

In addition to helping doctors discover and measure tumors, it also helps them develop better recovery and treatment plans. Recent MRI brain tumor segmentation algorithms have focused on U-Net design to combine high-level and low-level features for improved accuracy. Fully convolutional networks, which are also used for this purpose, are unable to successfully reconstruct the image through the decoder path because of the insufficient and low-level information from the encoder path. More effort needs to be done to optimise the low-level information flow from the encoder path to the decoder path

APA, Harvard, Vancouver, ISO, and other styles

45

Sharma, Udita, Neeraj Kumar, and Shafalii Sharma. "Deep Learning for Brain MRI Segmentation." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 11 (2023): 1–11. http://dx.doi.org/10.55041/ijsrem27209.

Full text

Abstract:

Brain magnetic resonance imaging (MRI) plays a central role in this setting, providing detailed anatomical information for clinical evaluation. Segmentation of her MRI images of the brain is an essential step to obtaining meaningful information for diagnostic and therapeutic purposes. Deep learning, especially convolutional neural networks (CNN), has emerged as a powerful solution to automate brain MRI segmentation and improve accuracy. In particular, convolutional neural networks have demonstrated remarkable performance in segmenting brain structures such as gray matter, white matter, and cer

APA, Harvard, Vancouver, ISO, and other styles

46

Liu, Zhipeng, Zhiming Zhang, Zhenvu Lei, Masaaki Omura, Rong-Long Wang, and Shangce Gao. "Dendritic Deep Learning for Medical Segmentation." IEEE/CAA Journal of Automatica Sinica 11, no. 3 (2024): 803–5. http://dx.doi.org/10.1109/jas.2023.123813.

Full text

APA, Harvard, Vancouver, ISO, and other styles

47

Isaksson, Lars Johannes, Paul Summers, Federico Mastroleo, et al. "Automatic Segmentation with Deep Learning in Radiotherapy." Cancers 15, no. 17 (2023): 4389. http://dx.doi.org/10.3390/cancers15174389.

Full text

Abstract:

This review provides a formal overview of current automatic segmentation studies that use deep learning in radiotherapy. It covers 807 published papers and includes multiple cancer sites, image types (CT/MRI/PET), and segmentation methods. We collect key statistics about the papers to uncover commonalities, trends, and methods, and identify areas where more research might be needed. Moreover, we analyzed the corpus by posing explicit questions aimed at providing high-quality and actionable insights, including: “What should researchers think about when starting a segmentation study?”, “How can

APA, Harvard, Vancouver, ISO, and other styles

48

Pécot, Thierry, Alexander Alekseyenko, and Kristin Wallace. "A deep learning segmentation strategy that minimizes the amount of manually annotated images." F1000Research 10 (March 30, 2021): 256. http://dx.doi.org/10.12688/f1000research.52026.1.

Full text

Abstract:

Deep learning has revolutionized the automatic processing of images. While deep convolutional neural networks have demonstrated astonishing segmentation results for many biological objects acquired with microscopy, this technology's good performance relies on large training datasets. In this paper, we present a strategy to minimize the amount of time spent in manually annotating images for segmentation. It involves using an efficient and open source annotation tool, the artificial increase of the training data set with data augmentation, the creation of an artificial data set with a conditiona

APA, Harvard, Vancouver, ISO, and other styles

49

Pécot, Thierry, Alexander Alekseyenko, and Kristin Wallace. "A deep learning segmentation strategy that minimizes the amount of manually annotated images." F1000Research 10 (January 17, 2022): 256. http://dx.doi.org/10.12688/f1000research.52026.2.

Full text

Abstract:

Deep learning has revolutionized the automatic processing of images. While deep convolutional neural networks have demonstrated astonishing segmentation results for many biological objects acquired with microscopy, this technology's good performance relies on large training datasets. In this paper, we present a strategy to minimize the amount of time spent in manually annotating images for segmentation. It involves using an efficient and open source annotation tool, the artificial increase of the training dataset with data augmentation, the creation of an artificial dataset with a conditional

APA, Harvard, Vancouver, ISO, and other styles

50

Liu, Xiangbin, Liping Song, Shuai Liu, and Yudong Zhang. "A Review of Deep-Learning-Based Medical Image Segmentation Methods." Sustainability 13, no. 3 (2021): 1224. http://dx.doi.org/10.3390/su13031224.

Full text

Abstract:

As an emerging biomedical image processing technology, medical image segmentation has made great contributions to sustainable medical care. Now it has become an important research direction in the field of computer vision. With the rapid development of deep learning, medical image processing based on deep convolutional neural networks has become a research hotspot. This paper focuses on the research of medical image segmentation based on deep learning. First, the basic ideas and characteristics of medical image segmentation based on deep learning are introduced. By explaining its research stat

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!