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Deep Learning and Artificial Intelligence Methods for Diagnostics in Medical Imaging and Sensing

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

Deadline for manuscript submissions: 30 September 2026 | Viewed by 2523

Special Issue Editors

Dr. Sebastian Iwaszenko

E-Mail Website
Guest Editor
Department of Acoustic, Electronic and IT Solutions, GIG National Research Institute, Gwarków 1, 40-166 Katowice, Poland
Interests: computer science; computer vision; image processing; image analysis; machine learning; artificial intelligence; software engineering
Special Issues, Collections and Topics in MDPI journals
Dr. Karolina Nurzynska

E-Mail Website
Guest Editor
Faculty of Automatic Control, Electronics and Computer Science, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland
Interests: machine learning; deep learning; image understanding; image analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Nowadays, the achievements in medicine and disease diagnosis based on sensors are extraordinary. Needless to say, progress would hardly be possible without the support of advanced technologies. The enormous possibilities in medical imaging and sensing, along with the development of medical image data handling methods, provide the foundations for helping patients and making people’s health and quality of life significantly better. Having said that, it is important to realize that we, as scientists, play a crucial role in this whole process.

Therefore, it is our pleasure to invite scholars to submit their research to this Special Issue of the MDPI journal Sensors, devoted to the applications of deep learning and computer vision in medical imaging and sensing. We are seeking to publish quality contributions to the field of uni- or multimodal medical image data analysis, the development of improvements in medical imaging quality, the diagnosis of diseases, supported or autonomous medical imagery data analysis, and many other vital areas. We welcome high-quality research papers addressing, but not limited to, medical imaging and sensing. We strongly believe that this Special Issue will be the place where exceptional scientific thoughts emerge and will establish new standards for knowledge and scientific experience sharing.

Prospective authors are invited to submit original manuscripts on sensors. Potential topics include but are not limited to:

  • Sensor-based medical image processing;
  • Computed tomography;
  • Micro-computed tomography;
  • Cone beam computed tomography;
  • Optical coherence tomography;
  • Magnetic resonance;
  • Ultrasonography;
  • Computer-aided diagnosis.

Dr. Sebastian Iwaszenko
Dr. Karolina Nurzynska
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 250 words) can be sent to the Editorial Office for assessment.

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

  • sensor-based medical image processing
  • computed tomography
  • micro-computed tomography
  • cone beam computed tomography
  • optical coherence tomography
  • magnetic resonance
  • ultrasonography
  • computer-aided diagnosis

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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24 pages, 1809 KB  
Article
Cloud-to-Edge Deployment of Optimized nnU-Net for Ischemic Stroke Lesion Segmentation on Resource-Constrained Embedded Devices
by Daniel Alcaraz Ortiz, Juan Francisco Zapata Pérez and Juan Martinez-Alajarin
Sensors 2026, 26(11), 3322; https://doi.org/10.3390/s26113322 (registering DOI) - 23 May 2026
Abstract
Ischemic stroke remains a leading cause of global mortality and long-term neurological disability, where the “Time is Brain” paradigm dictates that rapid and accurate lesion assessment is fundamental for effective clinical intervention. While the nnU-Net v2 framework has established a new state of [...] Read more.
Ischemic stroke remains a leading cause of global mortality and long-term neurological disability, where the “Time is Brain” paradigm dictates that rapid and accurate lesion assessment is fundamental for effective clinical intervention. While the nnU-Net v2 framework has established a new state of the art in medical image segmentation, its high computational demands and reliance on data-center-grade GPUs hinder its translation into real-time, point-of-care clinical workflows. This study presents a technical feasibility analysis of a Cloud-to-Edge optimization pipeline designed to transfer a 3D nnU-Net v2 model from a high-performance cloud environment to a resource-constrained embedded device. Experimental results showed that edge deployment was associated with a reduction in overlap-based segmentation metrics compared with the cloud reference, with Dice decreasing from approximately 0.78 to 0.67. However, TensorRT FP32 and FP16 inference produced nearly identical mean segmentation metrics, suggesting that reduced-precision inference did not introduce additional measurable degradation under the evaluated conditions. The optimized FP16 configuration achieved a processing time of 10.2 s per 3D volume, representing a 33% reduction compared with embedded FP32 inference, while operating within a low-power envelope of approximately 10–13 W. These findings support the preliminary technical feasibility of executing advanced 3D volumetric segmentation models on low-power edge hardware. Nevertheless, the evaluation was limited to an internal 25-case test subset and did not include external validation, prospective clinical assessment, or reader studies. Therefore, the proposed system should be interpreted as a preliminary deployment framework rather than a clinically validated tool for autonomous stroke imaging. Full article
(This article belongs to the Special Issue Deep Learning and Artificial Intelligence Methods for Diagnostics in Medical Imaging and Sensing)
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16 pages, 9023 KB  
Article
Optimising Camera–ChArUco Geometry for Motion Compensation in Standing Equine CT: A CT-Motivated Benchtop Study
by Cosimo Aliani, Cosimo Lorenzetto Bologna, Piergiorgio Francia and Leonardo Bocchi
Sensors 2026, 26(4), 1310; https://doi.org/10.3390/s26041310 - 18 Feb 2026
Viewed by 514
Abstract
Standing equine computed tomography (CT) acquisitions are susceptible to residual postural sway, which can introduce view-inconsistent motion and degrade image quality. External optical tracking based on ChArUco fiducials is a promising, low-cost strategy to enable projection-wise motion compensation, yet quantitative guidance on how [...] Read more.
Standing equine computed tomography (CT) acquisitions are susceptible to residual postural sway, which can introduce view-inconsistent motion and degrade image quality. External optical tracking based on ChArUco fiducials is a promising, low-cost strategy to enable projection-wise motion compensation, yet quantitative guidance on how camera–marker geometry affects pose-estimation performance remains limited. This CT-motivated benchtop study characterizes how the relative camera–ChArUco configuration influences both the accuracy (bias with respect to ground truth) and the precision (repeatability) of pose estimates obtained from RGB images using OpenCV ChArUco detection and reprojection-error minimization to estimate the rigid camera-to-board transformation. Controlled experiments systematically varied acquisition protocol (continuous repeated estimates at fixed pose versus cyclic repositioning), viewing angle over a wide angular range at two working distances, and camera-to-board distance over multiple depth settings. Ground truth for angular configurations was defined by a stepper-motor rotation stage, while distance ground truth was obtained by ruler measurements. Performance was summarized via mean absolute error and standard deviation across repeated measurements, complemented by variance-based statistical testing with multiple-comparison correction. Cyclic repositioning did not yield evidence of increased variability relative to continuous acquisitions, supporting view-by-view sampling. Viewing angle induced a consistent accuracy–precision trade-off for rotations: frontal views minimized mean error but exhibited higher variability, whereas oblique views reduced jitter at the expense of increased bias. Increasing working distance reduced repeatability, most prominently for depth-related components. Overall, these findings provide pre-clinical guidance for selecting camera/marker placement (moderately oblique viewpoints, limited working distance, sufficient image footprint) before in-scanner and in-vivo validation for standing equine CT motion compensation. Full article
(This article belongs to the Special Issue Deep Learning and Artificial Intelligence Methods for Diagnostics in Medical Imaging and Sensing)
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36 pages, 7233 KB  
Article
Deep Learning for Tumor Segmentation and Multiclass Classification in Breast Ultrasound Images Using Pretrained Models
by K. E. ArunKumar, Matthew E. Wilson, Nathan E. Blake, Tylor J. Yost and Matthew Walker
Sensors 2025, 25(24), 7557; https://doi.org/10.3390/s25247557 - 12 Dec 2025
Viewed by 1364
Abstract
Early detection of breast cancer commonly relies on imaging technologies such as ultrasound, mammography and MRI. Among these, breast ultrasound is widely used by radiologists to identify and assess lesions. In this study, we developed image segmentation techniques and multiclass classification artificial intelligence [...] Read more.
Early detection of breast cancer commonly relies on imaging technologies such as ultrasound, mammography and MRI. Among these, breast ultrasound is widely used by radiologists to identify and assess lesions. In this study, we developed image segmentation techniques and multiclass classification artificial intelligence (AI) tools based on pretrained models to segment lesions and detect breast cancer. The proposed workflow includes both the development of segmentation models and development of a series of classification models to classify ultrasound images as normal, benign or malignant. The pretrained models were trained and evaluated on the Breast Ultrasound Images (BUSI) dataset, a publicly available collection of grayscale breast ultrasound images with corresponding expert-annotated masks. For segmentation, images and ground-truth masks were used to pretrained encoder (ResNet18, EfficientNet-B0 and MobileNetV2)–decoder (U-Net, U-Net++ and DeepLabV3) models, including the DeepLabV3 architecture integrated with a Frequency-Domain Feature Enhancement Module (FEM). The proposed FEM improves spatial and spectral feature representations using Discrete Fourier Transform (DFT), GroupNorm, dropout regularization and adaptive fusion. For classification, each image was assigned a label (normal, benign or malignant). Optuna, an open-source software framework, was used for hyperparameter optimization and for the testing of various pretrained models to determine the best encoder–decoder segmentation architecture. Five different pretrained models (ResNet18, DenseNet121, InceptionV3, MobielNetV3 and GoogleNet) were optimized for multiclass classification. DeepLabV3 outperformed other segmentation architectures, with consistent performance across training, validation and test images, with Dice Similarity Coefficient (DSC, a metric describing the overlap between predicted and true lesion regions) values of 0.87, 0.80 and 0.83 on training, validation and test sets, respectively. ResNet18:DeepLabV3 achieved an Intersection over Union (IoU) score of 0.78 during training, while ResNet18:U-Net++ achieved the best Dice coefficient (0.83) and IoU (0.71) and area under the curve (AUC, 0.91) scores on the test (unseen) dataset when compared to other models. However, the proposed Resnet18: FrequencyAwareDeepLabV3 (FADeepLabV3) achieved a DSC of 0.85 and an IoU of 0.72 on the test dataset, demonstrating improvements over standard DeepLabV3. Notably, the frequency-domain enhancement substantially improved the AUC from 0.90 to 0.98, indicating enhanced prediction confidence and clinical reliability. For classification, ResNet18 produced an F1 score—a measure combining precision and recall—of 0.95 and an accuracy of 0.90 on the training dataset, while InceptionV3 performed best on the test dataset, with an F1 score of 0.75 and accuracy of 0.83. We demonstrate a comprehensive approach to automate the segmentation and multiclass classification of breast cancer ultrasound images into benign, malignant or normal transfer learning models on an imbalanced ultrasound image dataset. Full article
(This article belongs to the Special Issue Deep Learning and Artificial Intelligence Methods for Diagnostics in Medical Imaging and Sensing)
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