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:
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| Journal Title | APCs |
|---|---|
| Imaging and Radiation Research | $300 |
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Vol 7, No 2 (2024)
Table of Contents
Open Access
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.
Announcements
World Radiography Day 8 November 2024 - Importance, Theme & History |
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 @ww.pacehospital.com |
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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 |
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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. |
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| Posted: 2024-10-22 | More... |
Requirements for Documentation on Human and Animal Research |
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| 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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