Imaging and Radiation Research

ISSN:

2578-1618 (Online)

Journal Abbreviation:

Imaging. Radiat. Res.

Imaging and Radiation Research (IRR) is an international journal that aims to communicate to its readers the state-of-the-art technologies and methods of imaging and radiation. The journal welcomes original research works (laboratory-based works, modeling, field tests, case reports), reviews, and important applications of imaging technology and radiation-related analysis, and IRR is open to subjects in medical science, surgical practices, biomedical science, biology, materials science, engineering, as well as other related branches of physics and chemistry.

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

The use of SPECT and PET
Magnetic resonance imaging (MRI)
Ultrasonic imaging
Gamma camera and its application
Electron microscopy
Computed tomography
Electron imaging and processing
X-ray diffraction
Spectroscopic analysis
Radiation detection and measurement
Radiotherapy
Nuclear physics
Ionizing radiation

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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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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 Title	APCs
Imaging and Radiation Research	$800

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Vol 6, No 2 (2023)

Open Access

Article

Article ID: 6125

Abstract

PDF

Study on the connotation of traditional Chinese Daoist medicine culture

by Jun Zhang, Zhenxing Tang, Liang Wang, Qun Hu, Xiaowen Li

Imaging. Radiat. Res. 2023 , 6(2); 75 Views

doi: 10.24294/irr.v6i2.6125

Abstract This study aims to explore the connotation of Daoist medicine culture and investigate its relationship with modern medicine. Exploring the connotation of Daoist medicine culture is beneficial for advocating a healthy lifestyle, improving people’s physical and mental health, promoting individual comprehensive development, and enhancing happiness. By drawing wisdom and experience from Daoist medicine, inheriting various medical methods such as herbal treatment, acupuncture, massage, and integrating the concept of integrated Chinese and Western medicine into modern medicine, not only can treatment effectiveness be improved, but also interdisciplinary communication and cooperation can be promoted, thus driving the innovation and development of medical knowledge.

Open Access

Article

Article ID: 5757

Abstract

PDF

Performance evaluation of YOLOv5 and YOLOv8 models in car detection

by Fatma Nur Kılıçkaya, Murat Taşyürek, Celal Öztürk

Imaging. Radiat. Res. 2023 , 6(2); 316 Views

doi: 10.24294/irr.v6i2.5757

Abstract Vehicle detection stands out as a rapidly developing technology today and is further strengthened by deep learning algorithms. This technology is critical in traffic management, automated driving systems, security, urban planning, environmental impacts, transportation, and emergency response applications. Vehicle detection, which is used in many application areas such as monitoring traffic flow, assessing density, increasing security, and vehicle detection in automatic driving systems, makes an effective contribution to a wide range of areas, from urban planning to security measures. Moreover, the integration of this technology represents an important step for the development of smart cities and sustainable urban life. Deep learning models, especially algorithms such as You Only Look Once version 5 (YOLOv5) and You Only Look Once version 8 (YOLOv8), show effective vehicle detection results with satellite image data. According to the comparisons, the precision and recall values of the YOLOv5 model are 1.63% and 2.49% higher, respectively, than the YOLOv8 model. The reason for this difference is that the YOLOv8 model makes more sensitive vehicle detection than the YOLOv5. In the comparison based on the F1 score, the F1 score of YOLOv5 was measured as 0.958, while the F1 score of YOLOv8 was measured as 0.938. Ignoring sensitivity amounts, the increase in F1 score of YOLOv8 compared to YOLOv5 was found to be 0.06%.

Open Access

Article

Article ID: 6398

Abstract

PDF

Molecular dynamics simulation of atomic interaction between mediator protein of human prostate cancer and Fe/C720 buckyballs-statin structures

by Mohammad Pour Panah, Roozbeh Sabetvand

Imaging. Radiat. Res. 2023 , 6(2); 31 Views

doi: 10.24294/irr.v6i2.6398

Abstract Atomic interaction between mediator protein of human prostate cancer (PHPC) and Fe/C 720 Buckyballs-Statin is important for medical science. For the first time, we use molecular dynamics (MD) approach based on Newton’s formalism to describe the destruction of PHPC via Fe/C 720 Buckyballs-Statin with atomic accuracy. In this work, the atomic interaction of PHPC and Fe/C 720 Buckyballs-Statin introduced via equilibrium molecular dynamics approach. In this method, each PHPC and Fe/C 720 Buckyballs-Statin is defined by C, H, Cl, N, O, P, S, and Fe elements and contrived by universal force field (UFF) and DREIDING force-field to introduce their time evolution. The results of our studies regarding the dynamical behavior of these atom-base compounds have been reported by calculating the Potential energy, center of mass (COM) position, diffusion ratio and volume of defined systems. The estimated values for these quantities show the attraction force between Buckyball-based structure and protein sample, which COM distance of these samples changes from 10.27 Å to 2.96 Å after 10 ns. Physically, these interactions causing the destruction of the PHPC. Numerically, the volume of this biostructure enlarged from 665,276 Å 3 to 737,143 Å 3 by MD time passing. This finding reported for the first time which can be considered by the pharmaceutical industry. Simulations indicated the volume of the PHPC increases by Fe/C 720 Buckyballs-Statin diffusion into this compound. By enlarging this quantity (diffusion coefficient), the atomic stability of PHPC decreases and protein destruction procedure fulfilled.

Open Access

Article

Article ID: 6195

Abstract

PDF

Enhancing breast cancer detection in thermographic images using deep hybrid networks

by Rezazadeh Hanieh, Saniei Elham, Salehi Barough Mehdi

Imaging. Radiat. Res. 2023 , 6(2); 29 Views

doi: 10.24294/irr.v6i2.6195

Abstract Breast cancer was a prevalent form of cancer worldwide. Thermography, a method for diagnosing breast cancer, involves recording the thermal patterns of the breast. This article explores the use of a convolutional neural network (CNN) algorithm to extract features from a dataset of thermographic images. Initially, the CNN network was used to extract a feature vector from the images. Subsequently, machine learning techniques can be used for image classification. This study utilizes four classification methods, namely Fully connected neural network (FCnet), support vector machine (SVM), classification linear model (CLINEAR), and KNN, to classify breast cancer from thermographic images. The accuracy rates achieved by the FCnet, SVM, CLINEAR, and k-nearest neighbors (KNN) algorithms were 94.2%, 95.0%, 95.0%, and 94.1%, respectively. Furthermore, the reliability parameters for these classifiers were computed as 92.1%, 97.5%, 96.5%, and 91.2%, while their respective sensitivities were calculated as 95.5%, 94.1%, 90.4%, and 93.2%. These findings can assist experts in developing an expert system for breast cancer diagnosis.

Announcements


New Author Guidelines are updated
Please follow the journal's author guideline and the required article template to prepare your manuscript.
Posted: 2023-01-06	More...

Congratulations to one of Editorial Team Members
Shashanka Rajendrachari Department of Metallurgical and Materials Engineering, Bartin University Turkey
Posted: 2022-08-19	More...

The “Conflict of Interest” policy
For the sake of academic fairness, all authors are required to declare all activities that have the potential to be deemed as a source of competing interest in relations to their submitted manuscript. Examples of such activities could include personal or work-related relationships, events, etc. Authors who have nothing to declare are encouraged to add "The authors declare that there is no conflict of interest" in this section. A declaration of interests for all authors should be received before an article can be reviewed and accepted for the publication. As the authors, editors or reviewers, they also are required to declare the conflict of interest in academy.
Posted: 2022-02-10	More...