Zobrazeno 1 - 10
of 33
pro vyhledávání: '"Heba M. Emara"'
Publikováno v:
IEEE Access, Vol 11, Pp 108126-108151 (2023)
The efficient compression and classification of medical signals, particularly electroencephalography (EEG) and electrocardiography (ECG) signals in wireless body area network (WBAN) systems, are crucial for real-time monitoring and diagnosis. This pa
Externí odkaz:
https://doaj.org/article/b9c57a7a3d7b408d96687560294cd537
Autor:
Fatma Taher, Mohamed R. Shoaib, Heba M. Emara, Khaled M. Abdelwahab, Fathi E. Abd El-Samie, Mohammad T. Haweel
Publikováno v:
Frontiers in Public Health, Vol 10 (2022)
The brain tumor is an urgent malignancy caused by unregulated cell division. Tumors are classified using a biopsy, which is normally performed after the final brain surgery. Deep learning technology advancements have assisted the health professionals
Externí odkaz:
https://doaj.org/article/e85515e301f9401c8f277a0a5d94cb51
Autor:
Heba M. Emara, Mohamed R. Shoaib, Walid El-Shafai, Mohamed Elwekeil, Ezz El-Din Hemdan, Mostafa M. Fouda, Taha E. Taha, Adel S. El-Fishawy, El-Sayed M. El-Rabaie, Fathi E. Abd El-Samie
Publikováno v:
Diagnostics, Vol 13, Iss 7, p 1319 (2023)
Acute lower respiratory infection is a leading cause of death in developing countries. Hence, progress has been made for early detection and treatment. There is still a need for improved diagnostic and therapeutic strategies, particularly in resource
Externí odkaz:
https://doaj.org/article/80b5b4ae17d34ea7aa0d6a4964e8c42d
Autor:
Fatma M. Ghamry, Heba M. Emara, Ahmed Hagag, Walid El-Shafai, Ghada M. El-Banby, Moawad I. Dessouky, Adel S. El-Fishawy, Noha A. El-Hag, Fathi E. Abd El-Samie
Publikováno v:
Journal of Optics. 52:818-830
Autor:
Mohamed Elwekeil, Fathi E. Abd El-Samie, Heba M. Emara, Walid El-Shafai, Mohamed Shoaib, Adel S. El-Fishawy, Moawad I. Dessouky, Taha E. Taha, El-Sayed M. El-Rabaie, Saleh A. Alshebeili
Publikováno v:
Microscopy Research and Technique
This article is mainly concerned with COVID‐19 diagnosis from X‐ray images. The number of cases infected with COVID‐19 is increasing daily, and there is a limitation in the number of test kits needed in hospitals. Therefore, there is an imperat
Autor:
Fathi E. Abd El-Samie, Mohamed Elwekeil, Ghada M. El Banby, Walid El-Shafai, Taha E. Taha, Saleh A. Alshebeili, Heba M. Emara, Adel S. El-Fishawy, El-Sayed M. El-Rabaie, Turky N. Alotaiby
Publikováno v:
Annals of Data Science. 9:393-428
Seizure detection and prediction are a very hot topics in medical signal processing due to their importance in automatic medical diagnosis. This paper presents three efficient frameworks for applications related to electroencephalogram (EEG) signal p
Autor:
Fatma E. Ibrahim, Heba M. Emara, Walid El‐Shafai, Mohamed Elwekeil, Mohamed Rihan, Ibrahim M. Eldokany, Taha E. Taha, Adel S. El‐Fishawy, El‐Sayed M. El‐Rabaie, Essam Abdellatef, Fathi E. Abd El‐Samie
Publikováno v:
International Journal for Numerical Methods in Biomedical Engineering. 38
Electroencephalography (EEG) is among the main tools used for analyzing and diagnosing epilepsy. The manual analysis of EEG must be conducted by highly trained clinicians or neuro-physiologists; a process that is considered to have a comparatively lo
Autor:
Heba M. Emara, Mohamed Elwekeil, Adel S. El-Fishawy, Taha E. Taha, Saleh A. Alshebeili, El-Sayed M. El-Rabaie, Turky N. Alotaiby, Fathi E. Abd El-Samie
Publikováno v:
Wireless Personal Communications. 116:3371-3395
This paper is concerned with Electroencephalography (EEG) seizure prediction, which means the detection of the pre-ictal state prior to ictal activity occurrence. The basic idea of the proposed approach for EEG seizure prediction is to work on the si
Autor:
Mahmoud Elreefy, Walid El-Shafai, Adel S. El-Fishawy, Nagy W. Messiha, Waleed Al-Nuaimy, Osama Zahran, Zeinab M. Elsherbeny, Ahmed Sedik, Mohamed Elwakeil, Turky N. Alotaiby, Ashraf A. M. Khalaf, Fathi E. Abd El-Samie, Fatma Ibrahim, Asmaa Hamad, Heba A. El-Khobby, Ghada M. El Banby, Noha A. El-Hag, Mahmoud A. Attia, Heba M. Emara, Ali A. Khalil, Ibrahim M. Eldokany, Eman M. Shahin, Mohamed Rihan, Taha E. Taha, Moawad I. Dessouky, El-Sayed M. El-Rabaie, Saleh A. Alshebeili
Publikováno v:
International Journal of Speech Technology. 22:739-767
Anomaly detection is a very vital area in medical signal and image processing due to its importance in automatic diagnosis. This paper presents three efficient anomaly detection approaches for applications related to Electroencephalogram (EEG) signal