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TOPICAL COLLECTIONS

Innovating Brain Tumor Research and Treatment Through Artificial Intelligence and Machine Learning
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Guest Editors

M. Shahid Anwar

M. Shahid Anwar

Department of AI and Software, Gachon University South Korea

Muhammad Shahid Anwar is currently working as an Assistant Professor in the Department of AI & Software at Gachon University, Seongnam, South Korea. He received his Ph.D. degree in Information and Communication Engineering from Beijing Institute of Technology, Beijing, China in 2021 and his M.Sc. in Telecommunications Technology from Aston University, Birmingham, U.K., in 2012. Dr. Shahid has authored and co-authored more than 90 publications top journals, and flagship conference. Also published seven book chapters in reputed publisher. He has been honored with the “Outstanding Scholar of the Year 2020 Award" from the CSC Scholarship Council and “Excellent Student of the Year 2020 Award" from the Beijing Institute of Technology, China. He has been serving as an editorial board member and guest editor in several Journals. His research interests include AI Healthcare, immersive Technology (VR/AR/XR), Metaverse, and Quality of Experience (QoE) evaluations of VR application in different domains such as healthcare and education.  

Jaroslav Frnda

Jaroslav Frnda

University of Zilina, Zilina, Slovakia

Jaroslav Frnda (Senior Member, IEEE) was born in Slovakia, in 1989. He received the M.Sc. and Ph.D. degrees from the Department of Telecommunications, VSB—Technical University of Ostrava, Czech Republic, in 2013 and 2018, respectively. He is currently an Assistant Professor with the University of Žilina, Slovakia. He has authored or coauthored more than 85 journal articles in Web of Science. His research interests include the machine learning algorithms, AI healthcare, quality of multimedia services in IP networks, and data analysis. In 2022, he was the Finalist of the Category Outstanding Scientist in Slovakia under the age of 35-ESET Science Award, in 2022. 

 Mimonah Alqathrady

Mimonah Alqathrady

Najran University, Saudi Arabia

Mimonah Al Qathrady earned her Ph.D. in Computer Engineering (CE) from the University of Florida (UF), Gainesville, USA, in 2020. Prior to this, she completed her MSc in CE at UF in 2013, and received B.S. of Information Systems from King Khalid University, Abha, Saudi Arabia, in 2007. Since 2020, she has been working as an assistant professor at the Computer Science and Information Systems College at Najran University. Her research interest is on data-driven systems integrating machine and deep learning. She is also interested in AI Healthcare, IoT data analysis and modeling, with applications in mobility, encounter and infection tracing, and security.  She worked on several projects, such as MobiBench, as well as being the lead investigator in a multitude of projects, such as i-Hospital and AI in IoT. She was the coordinator of NOMADS lab at UF, and she is a member of ACM and IEEE. 

Collection Overview

Integrating artificial intelligence (AI) and machine learning (ML) into medical research and clinical practice has opened new avenues for understanding the complexities of brain tumors. This has led to advancements in early detection, personalized treatment plans, and improved patient outcomes. 

 

We are excited to announce a Methods Collection dedicated to the transformative role of AI and ML in the study, diagnosis, and treatment of brain tumors. This collection aims to bring together pioneering research that leverages AI and ML to address the multifaceted challenges posed by brain tumors.  

 

We invite contributions that explore a wide range of topics, including but not limited to: 

 

  • Advanced imaging techniques: Utilizing AI and ML to enhance the accuracy and resolution of brain tumor imaging, facilitating early and precise diagnosis 
  • Predictive modeling: Developing algorithms to predict tumor growth, patient prognosis, and response to treatment, enabling more effective and personalized therapeutic strategies 
  • Genomic and molecular profiling: Applying AI and ML to analyze genomic data, identifying biomarkers, and uncovering the molecular mechanisms underlying brain tumors 
  • Robotics and surgical interventions: Innovations in AI-driven robotic systems for neurosurgery, improving precision and outcomes in tumor resection 
  • AI in radiotherapy and chemotherapy: Optimizing treatment plans through machine learning models that predict and monitor the effectiveness and side effects of radiotherapy and chemotherapy 
  • Immersive technology-based treatment: Implementation of VR/MR/AR technologies and imaging reconstruction innovations in brain tumor treatment 

Articles

Augmented Multimodal Fusion for Optimized Brain Tumor Detection: Evaluation and Comparative Analysis

Augmented Multimodal Fusion for Optimized Brain Tumor Detection: Evaluation and Comparative Analysis

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Cited by 3

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2025

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Abstracts

Glioma Brain Tumor Segmentation using Innovative Attention-based models for Overall Survial Prediction

Niyaz Ahmad Wani*1

1Thapar Institute of Engineering and Technology, Patiala Punjab, India