Zobrazeno 1 - 10
of 698
pro vyhledávání: '"Class activation mapping"'
Publikováno v:
Open Engineering, Vol 14, Iss 1, Pp 285-305 (2024)
Electrocardiogram (ECG) recognition systems now play a leading role in the early detection of cardiovascular diseases. However, the explanation of judgments made by deep learning models in these systems is prominent for clinical acceptance. This arti
Externí odkaz:
https://doaj.org/article/33b541811fc64f87a757ac9980971d42
Publikováno v:
Automatika, Vol 65, Iss 3, Pp 1284-1299 (2024)
The fundus images of patients with Diabetic Retinopathy (DR) often display numerous lesions scattered across the retina. Current methods typically utilize the entire image for network learning, which has limitations since DR abnormalities are usually
Externí odkaz:
https://doaj.org/article/059486ffb1a14cacbd7e6e9a2406f1cc
Publikováno v:
Case Studies in Construction Materials, Vol 21, Iss , Pp e03643- (2024)
Automatic detection technology provides a reliable method for civil engineering distress detection. However, to overcome limitations of computational resources and the significant cost of image acquisition, this study proposes a simplified network pa
Externí odkaz:
https://doaj.org/article/7612fbe976ed4235a36856d76f057a0b
Publikováno v:
Frontiers in Neurorobotics, Vol 18 (2024)
Deep convolutional neural networks (CNNs) have achieved remarkable success in various computer vision tasks. However, the lack of interpretability in these models has raised concerns and hindered their widespread adoption in critical domains. Generat
Externí odkaz:
https://doaj.org/article/c2b7b5e7b3ea4249ae58fbb7ee9b920f
Autor:
Zhengkuan Zhao, Mingkuan Zhao, Tao Yang, Jie Li, Chao Qin, Ben Wang, Li Wang, Bing Li, Jun Liu
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-10 (2024)
Abstract Our main objective was to use machine learning methods to identify significant structural factors associated with pain severity in knee osteoarthritis patients. Additionally, we assessed the potential of various classes of imaging data using
Externí odkaz:
https://doaj.org/article/ff49711c1c2145f5ad5a267dc83b914b
Publikováno v:
Leida xuebao, Vol 13, Iss 2, Pp 428-442 (2024)
With the widespread application of deep learning methods in Synthetic Aperture Radar (SAR) image interpretation, the explainability of SAR target recognition deep networks has gradually attracted the attention of scholars. Class Activation Mapping (C
Externí odkaz:
https://doaj.org/article/7bed601a335d45c3a144509d27af965e
Publikováno v:
Leida xuebao, Vol 13, Iss 2, Pp 359-373 (2024)
Convolutional Neural Network (CNN) is widely used for image target classifications in Synthetic Aperture Radar (SAR), but the lack of mechanism transparency prevents it from meeting the practical application requirements, such as high reliability and
Externí odkaz:
https://doaj.org/article/a51e28940ae64aff874c67eb0d876b74
Autor:
Fan Zhong, Kaiqiao He, Mengqi Ji, Jianru Chen, Tianwen Gao, Shuli Li, Junpeng Zhang, Chunying Li
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-15 (2024)
Abstract Vitiligo is a hypopigmented skin disease characterized by the loss of melanin. The progressive nature and widespread incidence of vitiligo necessitate timely and accurate detection. Usually, a single diagnostic test often falls short of prov
Externí odkaz:
https://doaj.org/article/9073a335532043febcbabc57e0a7b9d8
Publikováno v:
Frontiers in Plant Science, Vol 15 (2024)
ProblemsPlant diseases significantly impact crop growth and yield. The variability and unpredictability of symptoms postinfection increase the complexity of image-based disease detection methods, leading to a higher false alarm rate.AimTo address thi
Externí odkaz:
https://doaj.org/article/a315df092c3f470a90f5de5311e667f5
Autor:
Reazul Hasan Prince, Abdul Al Mamun, Hasibul Islam Peyal, Shafiun Miraz, Md. Nahiduzzaman, Amith Khandakar, Mohamed Arselene Ayari
Publikováno v:
Frontiers in Plant Science, Vol 15 (2024)
Plant diseases significantly impact crop productivity and quality, posing a serious threat to global agriculture. The process of identifying and categorizing these diseases is often time-consuming and prone to errors. This research addresses this iss
Externí odkaz:
https://doaj.org/article/13d6be01c1544f1ab9544acbba95f356