Laya Mahmoudi
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Laya Mahmoudi has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Faeze Sadat Sadati Salimi
Department of Electrical Engineering, Faculty of Electrical Engineering and Computer Science, Babol Noshirvani University of Technology, Babol, Iran
1 shared publication - Jafar Abdollahi
Department of Computer Engineering, CT.C., Islamic Azad University, Tehran, Iran
1 shared publication - Mohammad Lagzian
Department of Management, Faculty of Economics and Administrative Sciences, Ferdowsi University of Mashhad, Mashhad, Iran.
1 shared publication - Parisa Mardukhian
Department of Computer Engineering, Zagros University of Kermanshah, Kermanshah, Iran
1 shared publication - Ramin Mousa
Zanjan University, Iran
1 shared publication
Latest Publications
- Open Access
- 22 January 2025
- Published by Springer Nature in Research Square
Enhancing Skin Cancer Diagnosis (SCD) Using Swin Transformer and Late Discrete Wavelet Transform (DWT)
Skin cancer is one of the dangerous cancers that threatens human life and can be seen in lesions on the patient’s skin. If this disease is not treated in time, it can threaten the patient’s life. However, with early detection of this disease, more effective treatment measures can be used and its progression can be prevented. Many machine learning and deep learning methods have been developed for early detection of skin cancer. But one of the advantages that can be provided by deep learning is its ability to learn from large data sets, which makes it more favorable for deep learning methods. The present paper proposes a method based on the combination of Swin transformer and Wavelet for classifying skin cancer images. In addition, feature optimization is performed using improved gray wolf optimizer(IGWO), Fox optimizer (FOX) and modified gorilla troops optimizer (MGTO) to identify optimal feature values. ISIC-2016 and ISIC-2017 datasets are used to evaluate the developed model. From the obtained results, it is shown that the Swin Transformer-based Wavelet +MGTO model provides the best result on the ISIC-2016 data set, achieving an accuracy of 0.9792. Furthermore, the Swin Transformer-based Wavelet +IGWO model provides the highest performance on the ISIC-2017 dataset, with an accuracy of 0.9792 and an F-measure score of 0.9780 on the ISIC-2017 data set.
- 22 January 2025
- Published by Springer Nature in Research Square
Open AccessEnhancing Skin Cancer Diagnosis (SCD) Using Swin Transformer and Late Discrete Wavelet Transform (DWT)
Skin cancer is one of the dangerous cancers that threatens human life and can be seen in lesions on the patient’s skin. If this disease is not treated in time, it can threaten the patient’s life. However, with early detection of this disease, more effective treatment measures can be used and its progression can be prevented. Many machine learning and deep learning methods have been developed for early detection of skin cancer. But one of the advantages that can be provided by deep learning is its ability to learn from large data sets, which makes it more favorable for deep learning methods. The present paper proposes a method based on the combination of Swin transformer and Wavelet for classifying skin cancer images. In addition, feature optimization is performed using improved gray wolf optimizer(IGWO), Fox optimizer (FOX) and modified gorilla troops optimizer (MGTO) to identify optimal feature values. ISIC-2016 and ISIC-2017 datasets are used to evaluate the developed model. From the obtained results, it is shown that the Swin Transformer-based Wavelet +MGTO model provides the best result on the ISIC-2016 data set, achieving an accuracy of 0.9792. Furthermore, the Swin Transformer-based Wavelet +IGWO model provides the highest performance on the ISIC-2017 dataset, with an accuracy of 0.9792 and an F-measure score of 0.9780 on the ISIC-2017 data set.
- Open Access
- 16 December 2024
- Published by Partners in Digital Health in Blockchain in Healthcare Today
- Vol. 7 (3)
Tracing the Blockchain Challenges in Healthcare: A Topic Modeling and Bibliometric Analysis
The application of blockchain technology to healthcare offers promise in providing solutions to some key challenges related to data sharing, privacy, security, and access control. There are however several barriers preventing blockchain adoption from spreading widely, which has prompted research efforts. This study aims to conduct a bibliometric analysis of 196 documents, indexed in the Scopus database, to examine the structure, impact, contributors, and journals. Also, topic modeling is applied to give new knowledge on the latent topical structure of this literature. As a result, five critical challenges have been identified regarding the use of blockchain in healthcare including data privacy/security, scalability, governance, interoperability and standards, and cost.
- 16 December 2024
- Published by Partners in Digital Health in Blockchain in Healthcare Today
- Vol. 7 (3)
Open AccessTracing the Blockchain Challenges in Healthcare: A Topic Modeling and Bibliometric Analysis
The application of blockchain technology to healthcare offers promise in providing solutions to some key challenges related to data sharing, privacy, security, and access control. There are however several barriers preventing blockchain adoption from spreading widely, which has prompted research efforts. This study aims to conduct a bibliometric analysis of 196 documents, indexed in the Scopus database, to examine the structure, impact, contributors, and journals. Also, topic modeling is applied to give new knowledge on the latent topical structure of this literature. As a result, five critical challenges have been identified regarding the use of blockchain in healthcare including data privacy/security, scalability, governance, interoperability and standards, and cost.