Imaging and Radiation Research


ISSN:

2578-1618 (Online)

Journal Abbreviation:

Imaging. Radiat. Res.

Imaging and Radiation Research (IRR, ISSN: 2578-1618) is an international journal dedicated to advancing the field of medical imaging, radiation sciences, and their applications in health and disease.

IRR is open to a broad range of subjects, spanning medical science, surgical practices, biomedical engineering, biology, materials science, environmental science, and related branches of physics and chemistry.

It welcomes original research contributions, including laboratory-based studies, modeling, field tests, case reports, reviews, and significant applications of imaging technology and radiation-related analysis.

The journal covers all aspects of imaging technology and analysis methods, Radiation Biology & Radiation Physics, including but not limited to:

  • The Application of SPECT and PET
  • Magnetic resonance imaging (MRI) Technologies and Advances
  • Ultrasonic imaging
  • Gamma camera and its application
  • Electron microscopy
  • Computed tomography (CT)
  • Electron imaging and processing techniques
  • X-ray diffraction
  • Spectroscopic analysis
  • Radiation detection and measurement
  • Radiotherapy
  • Nuclear physics
  • Ionizing radiation
  • Computer imaging techniques and applications
  • Artificial intelligence and machine learning in imaging
  • Biomedical engineering applications
  • Nanotechnology in imaging and therapy
  • Radiopharmaceuticals and their applications
  • Computational biology in image analysis
  • Environmental radiation monitoring
  • Health policy and management related to imaging and radiation
  • Psychological imaging techniques
  • Personalized medicine based on imaging results
  • Telemedicine and remote diagnostics using imaging technologies

 

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As part of the submission process, authors are required to check off their submission's compliance with all of the following items, and submissions may be returned to authors that do not adhere to these guidelines.

  1. The submission has not been previously published, nor is it before another journal for consideration (or an explanation has been provided in Comments to the Editor).
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  3. Where available, URLs for the references have been provided.
  4. The text adheres to the stylistic and bibliographic requirements outlined in the Author Guidelines, which is found in About the Journal.
  5. If submitting to a peer-reviewed section of the journal, the instructions in Ensuring a Blind Review have been followed.
 

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Imaging and Radiation Research is an Open Access Journal under EnPress Publisher. All articles published in Imaging and Radiation Research are accessible electronically from the journal website without commencing any kind of payment. In order to ensure contents are freely available and maintain publishing quality, Article Process Charges (APCs) are applicable to all authors who wish to submit their articles to the journal to cover the cost incurred in processing the manuscripts. Such cost will cover the peer-review, copyediting, typesetting, publishing, content depositing and archiving processes. Those charges are applicable only to authors who have their manuscript successfully accepted after peer-review.

Journal TitleAPCs
Imaging and Radiation Research$300

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Vol 7, No 2 (2024)

Table of Contents

Open Access
Article
Article ID: 8514
PDF
by Shaimaa E. Nassar, Ibrahim Yasser, Hanan M. Amer, Mohamed A. Mohamed
Imaging. Radiat. Res. 2024 , 7(2);    425 Views
Abstract Brain tumors are a primary factor causing cancer-related deaths globally, and their classification remains a significant research challenge due to the variability in tumor intensity, size, and shape, as well as the similar appearances of different tumor types. Accurate differentiation is further complicated by these factors, making diagnosis difficult even with advanced imaging techniques such as magnetic resonance imaging (MRI). Recent techniques in artificial intelligence (AI), in particular deep learning (DL), have improved the speed and accuracy of medical image analysis, but they still face challenges like overfitting and the need for large annotated datasets. This study addresses these challenges by presenting two approaches for brain tumor classification using MRI images. The first approach involves fine-tuning transfer learning cutting-edge models, including SEResNet, ConvNeXtBase, and ResNet101V2, with global average pooling 2D and dropout layers to minimize overfitting and reduce the need for extensive preprocessing. The second approach leverages the Vision Transformer (ViT), optimized with the AdamW optimizer and extensive data augmentation. Experiments on the BT-Large-4C dataset demonstrate that SEResNet achieves the highest accuracy of 97.96%, surpassing ViT’s 95.4%. These results suggest that fine-tuning and transfer learning models are more effective at addressing the challenges of overfitting and dataset limitations, ultimately outperforming the Vision Transformer and existing state-of-the-art techniques in brain tumor classification.
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Announcements

 

World Radiography Day 8 November 2024 - Importance, Theme & History

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Posted: 2024-11-08 More...
 

Fourth Committee Considers Effects of Atomic Radiation, Reaffirms Commitment to UN Scientific Committee, Hears Reports on Information, Peacekeeping

Reaffirming the important work of the United Nations Scientific Committee on the Effects of Atomic Radiation, the Fourth Committee (Special Political and Decolonization) today approved without a vote a resolution concerning that body, as speakers underscored the imperative of concerted and apolitical scientific work on this matter while also considering how to improve their working methods.

Posted: 2024-10-22 More...
 

Requirements for Documentation on Human and Animal Research

For articles submitted to the journal Imaging and Radiation Research that involve human or animal research, authors are required to provide the following documentation based on the specific circumstances:
Posted: 2024-08-18 More...
 
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