Zobrazeno 1 - 10
of 954
pro vyhledávání: '"Muhammad Fazal"'
Autor:
Yogesh Kumar, Supriya Shrivastav, Kinny Garg, Nandini Modi, Katarzyna Wiltos, Marcin Woźniak, Muhammad Fazal Ijaz
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-25 (2024)
Abstract Cancer detection poses a significant challenge for researchers and clinical experts due to its status as the leading cause of global mortality. Early detection is crucial, but traditional cancer detection methods often rely on invasive proce
Externí odkaz:
https://doaj.org/article/a6c5c2cfb8b44156b4a43c688eb2a0e1
Publikováno v:
Egyptian Liver Journal, Vol 14, Iss 1, Pp 1-4 (2024)
Abstract Background Bouveret syndrome is a rare etiology of gastric outlet obstruction, presenting with clinical manifestations that resemble those of several gastric pathologies. Timely diagnosis is imperative to mitigate potentially fatal consequen
Externí odkaz:
https://doaj.org/article/611f2b4195f64b6797c9ca3c4e5aa7a3
Autor:
Parvathaneni Naga Srinivasu, G. Jaya Lakshmi, Sujatha Canavoy Narahari, Jana Shafi, Jaeyoung Choi, Muhammad Fazal Ijaz
Publikováno v:
Egyptian Informatics Journal, Vol 27, Iss , Pp 100530- (2024)
The precise classification of medical images is crucial in various healthcare applications, especially in fields like disease diagnosis and treatment planning. In recent times, machine-intelligent models are desired to work in remote settings. Howeve
Externí odkaz:
https://doaj.org/article/83644f9a3aea429891adc473a609c9f3
Autor:
Priya Bhardwaj, SeongKi Kim, Apeksha Koul, Yogesh Kumar, Ankur Changela, Jana Shafi, Muhammad Fazal Ijaz
Publikováno v:
Frontiers in Oncology, Vol 14 (2024)
IntroductionThe rapid advancement of science and technology has significantly expanded the capabilities of artificial intelligence, enhancing diagnostic accuracy for gastric cancer.MethodsThis research aims to utilize endoscopic images to identify va
Externí odkaz:
https://doaj.org/article/abdf09da82514212bc9e5bbe1273aa1e
Autor:
Arindam Halder, Sanghita Gharami, Priyangshu Sadhu, Pawan Kumar Singh, Marcin Woźniak, Muhammad Fazal Ijaz
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-19 (2024)
Abstract In recent years, the growth spurt of medical imaging data has led to the development of various machine learning algorithms for various healthcare applications. The MedMNISTv2 dataset, a comprehensive benchmark for 2D biomedical image classi
Externí odkaz:
https://doaj.org/article/9bff8f1bf80d482bae8c98135b2b2caf
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-20 (2024)
Abstract Identifying and recognizing the food on the basis of its eating sounds is a challenging task, as it plays an important role in avoiding allergic foods, providing dietary preferences to people who are restricted to a particular diet, showcasi
Externí odkaz:
https://doaj.org/article/fcf3c55e2b8b46db93f24fc2ed584f56
Autor:
Yogesh Kumar, Pertik Garg, Manu Raj Moudgil, Rupinder Singh, Marcin Woźniak, Jana Shafi, Muhammad Fazal Ijaz
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-29 (2024)
Abstract Parasitic organisms pose a major global health threat, mainly in regions that lack advanced medical facilities. Early and accurate detection of parasitic organisms is vital to saving lives. Deep learning models have uplifted the medical sect
Externí odkaz:
https://doaj.org/article/2f56699c9a0a4e1d8d8f322de77e1b19
Publikováno v:
The Egyptian Heart Journal, Vol 76, Iss 1, Pp 1-4 (2024)
Abstract Background The most prevalent cyanotic congenital heart disease is Tetralogy of Fallot (TOF). It has a variety of presentations and is made up of four anatomic abnormalities. Documented literature shows an incidence of 13–20% of a right ao
Externí odkaz:
https://doaj.org/article/4f0b475e92ac4832af05018e252c386f
Autor:
Zari Farhadi, Mohammad-Reza Feizi-Derakhshi, Hossein Bevrani, Wonjoon Kim, Muhammad Fazal Ijaz
Publikováno v:
IEEE Access, Vol 12, Pp 33361-33383 (2024)
Ensembling is a powerful technique to obtain the most accurate results. In some cases, the large number of learners in ensemble learning mostly increases both computational load during the test phase and error rate. To solve this problem, in this pap
Externí odkaz:
https://doaj.org/article/bf75c25d4a2f41b5b6f138a99071d39c
Publikováno v:
IEEE Access, Vol 12, Pp 26183-26195 (2024)
The introduction of pre-trained large language models (LLMs) has transformed NLP by fine-tuning task-specific datasets, enabling notable advancements in news classification, language translation, and sentiment analysis. This has revolutionized the fi
Externí odkaz:
https://doaj.org/article/f72ee79bf37f4394986a53e61f5060df