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Medical Imaging and Sensing Technologies
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Medical Imaging and Sensing Technologies

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensing and Imaging".

Deadline for manuscript submissions: 31 December 2024 | Viewed by 6822

Special Issue Editors

Dr. Cihui Yang

E-Mail Website
Guest Editor
School of Information Engineering, Nanchang Hangkong University, Nanchang 330063, China
Interests: computer vision; pattern recognition; deep learning; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Shiping Wen

E-Mail Website
Guest Editor
Australian Artificial Intelligence Institute, Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW 2007, Australia
Interests: neural network; memristor system; deep learning; intelligent control
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Medical imaging as a non-invasive technology which can produce 2D or 3D anatomical images in vivo. Thanks to its high spatial resolution and superior expressive ability for anatomic structures, medical imaging has been widely used for disease detection, diagnosis, and treatment monitoring. However, acquiring useful information from medical images automatically and obtaining more higher-quality images regarding human health through medical imaging are still great challenges.

This Special Issue of Sensors on "Medical Imaging and Sensing Technologies" will collect high-quality original contributions focusing on innovative aspects of research and development related to medical imaging and sensing technologies at any stage, i.e., from laboratory to the clinic. Of particular relevance are imaging modalities and related sensing technologies (e.g., all types of optical imaging, photoacoustic, ultrasounds, computerized tomography, magnetic resonance imaging, positron emission tomography, etc.) for preclinical and clinical applications, as well as related image processing, analysis (e.g., tumor detection, image segmentation, reconstruction) and visualization algorithms. Advanced approaches related to multimodality imaging, image-guided therapy, and computational planning are especially welcome.

Relevant topics include but are not limited to:

  • Magnetic Resonance Imaging
  • Computerized Tomography
  • Positron Emission Tomography
  • X-rays
  • Ultrasounds
  • Medical image segmentation
  • Tumor detection
  • Image-guided radiotherapy
  • Medical image reconstruction
  • Medical image visualization
  • Medical image denoising

Dr. Cihui Yang
Prof. Dr. Shiping Wen
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • medical imaging
  • magnetic resonance imaging
  • medical image denoising
  • medical image segmentation
  • tumor detection
  • deep learning
  • image processing
  • computerized tomography
  • ultrasounds
  • positron emission tomography

Published Papers (3 papers)

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Research

23 pages, 2003 KiB  
Article
Advancing Brain Tumor Classification through Fine-Tuned Vision Transformers: A Comparative Study of Pre-Trained Models
by Abdullah A. Asiri, Ahmad Shaf, Tariq Ali, Muhammad Ahmad Pasha, Muhammad Aamir, Muhammad Irfan, Saeed Alqahtani, Ahmad Joman Alghamdi, Ali H. Alghamdi, Abdullah Fahad A. Alshamrani, Magbool Alelyani and Sultan Alamri
Sensors 2023, 23(18), 7913; https://doi.org/10.3390/s23187913 - 15 Sep 2023
Cited by 1 | Viewed by 2173
Abstract
This paper presents a comprehensive study on the classification of brain tumor images using five pre-trained vision transformer (ViT) models, namely R50-ViT-l16, ViT-l16, ViT-l32, ViT-b16, and ViT-b32, employing a fine-tuning approach. The objective of this study is to advance the state-of-the-art in brain [...] Read more.
This paper presents a comprehensive study on the classification of brain tumor images using five pre-trained vision transformer (ViT) models, namely R50-ViT-l16, ViT-l16, ViT-l32, ViT-b16, and ViT-b32, employing a fine-tuning approach. The objective of this study is to advance the state-of-the-art in brain tumor classification by harnessing the power of these advanced models. The dataset utilized for experimentation consists of a total of 4855 images in the training set and 857 images in the testing set, encompassing four distinct tumor classes. The performance evaluation of each model is conducted through an extensive analysis encompassing precision, recall, F1-score, accuracy, and confusion matrix metrics. Among the models assessed, ViT-b32 demonstrates exceptional performance, achieving a high accuracy of 98.24% in accurately classifying brain tumor images. Notably, the obtained results outperform existing methodologies, showcasing the efficacy of the proposed approach. The contributions of this research extend beyond conventional methods, as it not only employs cutting-edge ViT models but also surpasses the performance of existing approaches for brain tumor image classification. This study not only demonstrates the potential of ViT models in medical image analysis but also provides a benchmark for future research in the field of brain tumor classification. Full article
(This article belongs to the Special Issue Medical Imaging and Sensing Technologies)
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19 pages, 3648 KiB  
Article
Sooty Tern Optimization Algorithm-Based Deep Learning Model for Diagnosing NSCLC Tumours
by Muhammad Asim Saleem, Ngoc Thien Le, Widhyakorn Asdornwised, Surachai Chaitusaney, Ashir Javeed and Watit Benjapolakul
Sensors 2023, 23(4), 2147; https://doi.org/10.3390/s23042147 - 14 Feb 2023
Cited by 7 | Viewed by 1991
Abstract
Lung cancer is one of the most common causes of cancer deaths in the modern world. Screening of lung nodules is essential for early recognition to facilitate treatment that improves the rate of patient rehabilitation. An increase in accuracy during lung cancer detection [...] Read more.
Lung cancer is one of the most common causes of cancer deaths in the modern world. Screening of lung nodules is essential for early recognition to facilitate treatment that improves the rate of patient rehabilitation. An increase in accuracy during lung cancer detection is vital for sustaining the rate of patient persistence, even though several research works have been conducted in this research domain. Moreover, the classical system fails to segment cancer cells of different sizes accurately and with excellent reliability. This paper proposes a sooty tern optimization algorithm-based deep learning (DL) model for diagnosing non-small cell lung cancer (NSCLC) tumours with increased accuracy. We discuss various algorithms for diagnosing models that adopt the Otsu segmentation method to perfectly isolate the lung nodules. Then, the sooty tern optimization algorithm (SHOA) is adopted for partitioning the cancer nodules by defining the best characteristics, which aids in improving diagnostic accuracy. It further utilizes a local binary pattern (LBP) for determining appropriate feature retrieval from the lung nodules. In addition, it adopts CNN and GRU-based classifiers for identifying whether the lung nodules are malignant or non-malignant depending on the features retrieved during the diagnosing process. The experimental results of this SHOA-optimized DNN model achieved an accuracy of 98.32%, better than the baseline schemes used for comparison. Full article
(This article belongs to the Special Issue Medical Imaging and Sensing Technologies)
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18 pages, 6428 KiB  
Article
A 2D-GRAPPA Algorithm with a Boomerang Kernel for 3D MRI Data Accelerated along Two Phase-Encoding Directions
by Seonyeong Shin, Yeji Han and Jun-Young Chung
Sensors 2023, 23(1), 93; https://doi.org/10.3390/s23010093 - 22 Dec 2022
Viewed by 1772
Abstract
For the reconstruction of 3D MRI data that are accelerated along the two phase-encoding directions, the 2D-generalized autocalibrating partially parallel acquisitions (GRAPPA) algorithm can be used to estimate the missing data in the k-space. We propose a new boomerang-shaped kernel based on theoretic [...] Read more.
For the reconstruction of 3D MRI data that are accelerated along the two phase-encoding directions, the 2D-generalized autocalibrating partially parallel acquisitions (GRAPPA) algorithm can be used to estimate the missing data in the k-space. We propose a new boomerang-shaped kernel based on theoretic and systemic analyses of the shape and dimensions of the kernel. The reconstruction efficiency of the 2D-GRAPPA algorithm with the proposed boomerang-shaped kernel (i.e., boomerang kernel (BK)-2D-GRAPPA) was compared with other 2D-GRAPPA algorithms that utilize different types of kernels (i.e., EX-2D-GRAPPA and SK-2D-GRAPPA) based on computer simulation, phantom and in vivo experiments. The proposed method was validated for different sets of ACS lines with acceleration factors from four to eight and various sizes of the kernels. A quantitative analysis was also performed by comparing the normalized root mean squared error (nRMSE) in the images and the undersampled edges. Computer simulation, in vivo and phantom experiments, and the quantitative analysis, showed that the proposed method could reduce aliasing artifacts without reducing the SNRs of the reconstructed images. Full article
(This article belongs to the Special Issue Medical Imaging and Sensing Technologies)
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