Zobrazeno 1 - 10
of 815
pro vyhledávání: '"Image augmentation"'
Autor:
A. M. J. MD. Zubair Rahman, R. Mythili, K. Chokkanathan, T. R. Mahesh, K. Vanitha, Temesgen Engida Yimer
Publikováno v:
BMC Medical Imaging, Vol 24, Iss 1, Pp 1-15 (2024)
Abstract The early detection and diagnosis of gastrointestinal tract diseases, such as ulcerative colitis, polyps, and esophagitis, are crucial for timely treatment. Traditional imaging techniques often rely on manual interpretation, which is subject
Externí odkaz:
https://doaj.org/article/a70de337c7f2453a9a588b0f84ea073a
Autor:
Seunghyeon Wang
Publikováno v:
Results in Engineering, Vol 24, Iss , Pp 103571- (2024)
The detection of windows and their states from building façade images is crucial for applications such as digital twins and building performance simulations. Deep learning using object detection algorithms can achieve this with reliable accuracy, bu
Externí odkaz:
https://doaj.org/article/1284a76ebd2442838966d70627be4c42
Ensemble transfer learning meets explainable AI: A deep learning approach for leaf disease detection
Autor:
Hetarth Raval, Jyotismita Chaki
Publikováno v:
Ecological Informatics, Vol 84, Iss , Pp 102925- (2024)
Global food security is threatened by plant diseases and manual detection methods are often labor-intensive and time-consuming. Deep learning offers a promising solution by enabling early and accurate detection of leaf diseases. This study presents a
Externí odkaz:
https://doaj.org/article/064df6a822d4442f9f69609711a1a4d1
Autor:
Sadeena Sabnam, Sivakumar Rajagopal
Publikováno v:
Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, Vol 12, Iss 1 (2024)
In deep learning, GANs (Generative Adversarial Networks) are one of the prominent study areas due to their ability to generate synthetic data thereby solving the problem of the unavailability and the limited data sets. GAN is a framework of deep neur
Externí odkaz:
https://doaj.org/article/902a0407887642e38b8150b01c19e386
Publikováno v:
AI, Vol 5, Iss 2, Pp 576-593 (2024)
In this study, we developed and explored a methodical image augmentation technique for swimmer localisation in northern German outdoor lake environments. When it comes to enhancing swimmer safety, a main issue we have to deal with is the lack of real
Externí odkaz:
https://doaj.org/article/b1349c4348d04c57844a589972d50888
Publikováno v:
Data in Brief, Vol 55, Iss , Pp 110720- (2024)
Accurate inspection of rebars in Reinforced Concrete (RC) structures is essential and requires careful counting. Deep learning algorithms utilizing object detection can facilitate this process through Unmanned Aerial Vehicle (UAV) imagery. However, t
Externí odkaz:
https://doaj.org/article/7a7db2b136404e24a4ee69272e90f0f4
Autor:
Karzan Barzan Aqdar, Rawand Kawa Mustafa, Zhiyar Hamid Abdulqadir, Peshraw Ahmed Abdalla, Abdalbasit Mohamad Qadir, Alla Abdulqader Shali, Nariman Muhamad Aziz
Publikováno v:
Data in Brief, Vol 55, Iss , Pp 110633- (2024)
This data article presents a comprehensive dataset comprising breast cancer images collected from patients, encompassing two distinct sets: one from individuals diagnosed with breast cancer and another from those without the condition. Expert physici
Externí odkaz:
https://doaj.org/article/2c0f5ea9408f4909b3e4fcb952bbb675
YOLO-Based Image Segmentation for the Diagnostic of Spondylolisthesis From Lumbar Spine X-Ray Images
Autor:
Arnik Vephasayanant, Anuchit Jitpattanakul, Paisarn Muneesawang, Konlakorn Wongpatikaseree, Narit Hnoohom
Publikováno v:
IEEE Access, Vol 12, Pp 182242-182258 (2024)
Spondylolisthesis, a condition characterized by vertebral slippage, often results in pain and limited mobility. To enhance the detection of spondylolisthesis in X-ray images, we developed a YOLOv8-based model trained on a dataset of 10,616 images (AP
Externí odkaz:
https://doaj.org/article/6f3109fbc9444aedb9606ddace08107b
Publikováno v:
IEEE Access, Vol 12, Pp 182171-182189 (2024)
Deep neural networks are sensitive to distribution shifts, such as common corruption and adversarial examples, which occur across various frequency spectra. Numerous studies have been conducted to improve model robustness in the frequency domain. How
Externí odkaz:
https://doaj.org/article/2d4df70f7f8a4ac997b610ab2487e4ea
Autor:
Zahid Ur Rahman, Mohd Shahrimie Mohd Asaari, Haidi Ibrahim, Intan Sorfina Zainal Abidin, Mohamad Khairi Ishak
Publikováno v:
IEEE Access, Vol 12, Pp 179912-179943 (2024)
Enhancing model performance in agricultural image analysis faces challenges due to limited datasets, biological variability, and uncontrolled environments. Deep learning models require large, realistic datasets, which are often difficult to obtain. D
Externí odkaz:
https://doaj.org/article/29434bf58e574f7ea3939ec9d8cb6084