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Medical Imaging: Artificial Intelligence, Image Recognition, and Machine Learning Techniques (2nd Edition）

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

Deadline for manuscript submissions: 30 September 2026 | Viewed by 6109

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Dr. Ines Domingues

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Medical Physics, Radiobiology and Radiological Protection Group, Research Center of the Portuguese Institute of Oncology of Porto (CI-IPOP), 4200-072 Porto, Portugal
Interests: pattern recognition; image processing; biomedical applications; data science; artificial intelligence; machine learning; deep learning
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Dear Colleagues,

Medical imaging has become an essential component in many fields of medical research and clinical practice. Medical imaging techniques, deep learning, and artificial intelligence bring many healthcare protection benefits. We can now collect, measure, and analyse vast volumes of health-related data using computing, networking technologies, and artificial intelligence, leading to tremendous advances in healthcare and excellent opportunities for medical imaging communities. Meanwhile, these technologies have also brought new challenges and issues.

This Special Issue of the Journal Sensors is focused on advanced techniques, new challenges, and issues in Medical imaging.

Dr. Ines Domingues
Guest Editor

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Keywords

medical imaging
artificial intelligence
information fusion for medical data
image recognition
machine learning
deep learning
image processing
image analysis
computer vision

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Medical Imaging: Artificial Intelligence, Image Recognition, and Machine Learning Techniques in Sensors (8 articles)

Published Papers (4 papers)

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Research

22 pages, 13702 KB

Open AccessArticle

Three-Dimensional Analysis of Volumetric Changes During Palatal Wound Healing After Soft-Tissue Harvesting

by Viviana Desantis, Marco Brando Mario Paracchini, Fulvio Gatti, Carlo Pezzoli, Elena Maria Varoni and Marco Marcon

Sensors 2026, 26(16), 5088; https://doi.org/10.3390/s26165088 - 11 Aug 2026

Viewed by 252

Abstract

The growing demand for aesthetic and functional outcomes in periodontal and implant procedures has increased the use of connective tissue grafts harvested from the palate. Objective monitoring of the donor site remains challenging because healing involves progressive three-dimensional morphological changes and visible chromatic variations of the mucosal surface. This pilot feasibility study proposes an optical sensing and computer-vision workflow based on longitudinal intraoral scanner acquisitions to quantify palatal wound healing after soft-tissue harvesting. A TRIOS 3 intraoral scanner was used as a non-invasive sensing device to acquire textured three-dimensional datasets of the palatal donor area at baseline, immediately after surgery, and during follow-up at 1 and 2 weeks and at 1 and 3 months. The scanner output, consisting of surface geometry and color information, was processed as multimodal 3D data. The workflow included mesh preprocessing, reference-based registration of serial scans, robust alignment using geometric, normal, and chromatic cues, region-of-interest identification, and signed volumetric difference computation. Sensor-related uncertainty was addressed through an additional in vivo repeatability assessment on healthy volunteers and by comparison with previously published accuracy and precision data for the TRIOS scanner series. The resulting volumetric maps and healing curves enabled objective quantification of local tissue loss, swelling, and progressive recovery over time. Chromatic information from the textured scans was also considered as an additional sensing descriptor of mucosal healing. By integrating intraoral optical sensing, three-dimensional surface reconstruction, multimodal mesh registration, and quantitative volumetric analysis, the proposed approach provides a preliminary non-invasive framework for monitoring palatal donor-site healing. Six patients undergoing palatal soft-tissue harvesting were monitored using a TRIOS 3 intraoral scanner at baseline, immediately after surgery, and during follow-up at 1 and 2 weeks and at 1 and 3 months. Although all clinically evaluated patients exhibited complete clinical re-epithelialization by 3 months, quantitative 3D analysis revealed a persistent residual volumetric deficit, indicating that visual healing does not necessarily correspond to complete soft-tissue volume restoration. This sensor-based methodology may support clinical follow-up, improve the objectivity of soft-tissue assessment, and contribute to the development of digital biomarkers for oral wound healing. Full article

(This article belongs to the Special Issue Medical Imaging: Artificial Intelligence, Image Recognition, and Machine Learning Techniques (2nd Edition）)

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26 pages, 7267 KB

Open AccessArticle

A Hybrid U-Shaped Deep Learning Network for Intracerebral Hemorrhage Segmentation in CT Scans

by Ming Deng, Jiazuo Yao, Qingxiang Wu, Shihua Liang, Hailing Liang and Haihua Tang

Sensors 2026, 26(13), 4164; https://doi.org/10.3390/s26134164 - 2 Jul 2026

Viewed by 468

Abstract

Computed tomography (CT) scan is a widely used, non-invasive, sensor-based imaging technique that provides critical intracranial information for rapid stroke assessment. Accurate segmentation of intracerebral hemorrhage (ICH) in sensor-derived CT images is vital for clinical decision-making. Effective intelligent analysis of CT images is key to achieving reliable computer-aided diagnosis. However, existing deep learning methods struggle with complex ICH lesions characterized by blurred boundaries, irregular shapes, and large-scale variations. To address these challenges, this paper proposes TransAMGNet, a hybrid U-shaped network with Transformer integration for ICH CT image segmentation. The network is built on a residual U-Net backbone and introduces a Transformer encoder to strengthen global context modeling, thereby improving the representation of complex lesion morphology. Specifically, in the encoding stage, we design an Adaptive Dual-branch Channel Attention Module (ADCAM), which jointly models global and local channel information to enhance the model’s sensitivity to important feature responses. In the skip-connection pathway, we introduce a Multi-scale Feature Enhancement Module (MFEM), which preserves high-resolution spatial details while supplementing multi-scale contextual information to improve shallow-deep feature fusion. During decoding, a Gate-enhanced Dynamic Upsampling Module (GDUM) is constructed to improve the recovery of lesion boundaries and fine-grained structures through the synergy of gated recalibration and content-aware upsampling. The proposed method is systematically evaluated through comparative experiments and ablation studies. Experimental results show that TransAMGNet outperforms competing methods across multiple evaluation metrics, achieving Dice, Recall, IoU, Precision, and HD95 values of 90.47 ± 0.58%, 87.83 ± 3.71%, 81.26 ± 0.78%, 91.13 ± 0.95%, and 32.94 ± 1.1, respectively. The ablation studies further verify the effectiveness of each module. These results demonstrate that TransAMGNet can effectively improve segmentation performance for complex ICH lesions. Full article

(This article belongs to the Special Issue Medical Imaging: Artificial Intelligence, Image Recognition, and Machine Learning Techniques (2nd Edition）)

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26 pages, 2186 KB

Open AccessArticle

Cross-Sensor and Cross-Population Generalization of Deep Learning Models for Digital Mammography: A Controlled Four-Country Benchmark of Five Backbone Architectures with Statistical Significance Testing

by Somprasonk Gabbualoy, Pattarapong Phasukkit and Supan Tungjitkusolmun

Sensors 2026, 26(12), 3911; https://doi.org/10.3390/s26123911 - 19 Jun 2026

Viewed by 441

Abstract

Background/Objectives: Deep learning models for digital mammography sensor data are increasingly deployed across hospitals using different X-ray detector technologies and patient populations. Whether models trained on one sensor platform and population maintain accuracy when transferred to another has not been tested for the latest generation of mammography-specific foundation models under one controlled protocol. Methods: We fine-tuned five backbone architectures (ResNet-50, DINOv2-B14, Rad-DINO, Mammo-CLIP B5, and Mammo-FM) on CBIS-DDSM (film-digitized, USA, n = 714 validation) with three seeds, ablated a density-aware focal loss across three auxiliary weights, and evaluated transfer to three external sensor cohorts: CMMD (full-field digital, China, n = 1032), DMID (mixed digital, India, n = 509), and MIAS (film-digitized, UK, n = 322). Significance used paired DeLong z-tests with Benjamini–Hochberg FDR correction; temperature scaling tested post hoc recalibration at all transfer targets. Results: Within this single-source three-seed evaluation, ResNet-50 outperformed all four foundation models on CBIS-DDSM (AUC 0.867 vs. 0.847, 0.846, 0.813, and 0.703; all gaps p_adj < 0.05). The density-aware focal loss degraded both AUC and calibration at every weight tested. At transfer, every model lost 0.165 to 0.320 AUC points relative to in-distribution performance, with sensitivity at 95% specificity collapsing from 0.31 to 0.47 in-distribution to 0.11 to 0.22 across the three external targets. A per-seed Stouffer meta-analysis confirms that Mammo-CLIP B5 and Mammo-FM significantly outperformed ResNet-50 on DMID and Mammo-CLIP on CMMD, after BH-FDR; MIAS comparisons remained directional only. In the extremely dense subgroup (BI-RADS D4), Mammo-FM reached AUC 0.870 versus ResNet-50 at 0.842, a directional observation whose 95% CIs overlap heavily at the n = 140 sample size and which we do not interpret as a statistically supported advantage. Conclusions: In this single training-source, three-seed protocol, mammography-specific pretraining did not deliver the in-distribution AUC premium reported in the originating papers, and no architecture reached a level at which transfer deployment without local validation would be defensible. We frame these as observations specific to the present protocol rather than as broader conclusions about foundation models for mammography classification. The findings argue for sensor-stratified and population-stratified external validation and for local recalibration as practical prerequisites before clinical use. Code and weights are released under MIT license. Full article

(This article belongs to the Special Issue Medical Imaging: Artificial Intelligence, Image Recognition, and Machine Learning Techniques (2nd Edition）)

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28 pages, 2869 KB

Open AccessArticle

Enhancing Medical Image Segmentation and Classification Using a Fuzzy-Driven Method

by Akmal Abduvaitov, Abror Shavkatovich Buriboev, Djamshid Sultanov, Shavkat Buriboev, Ozod Yusupov, Kilichov Jasur and Andrew Jaeyong Choi

Sensors 2025, 25(18), 5931; https://doi.org/10.3390/s25185931 - 22 Sep 2025

Cited by 4 | Viewed by 3742

Abstract

Automated analysis for tumor segmentation and illness classification is hampered by the noise, low contrast, and ambiguity that are common in medical pictures. This work introduces a new 12-step fuzzy-based improvement pipeline that uses fuzzy entropy, fuzzy standard deviation, and histogram spread functions to enhance picture quality in CT, MRI, and X-ray modalities. The pipeline produces three improved versions per dataset, lowering BRISQUE scores from 28.8 to 21.7 (KiTS19), 30.3 to 23.4 (BraTS2020), and 26.8 to 22.1 (Chest X-ray). It is tested on KiTS19 (CT) for kidney tumor segmentation, BraTS2020 (MRI) for brain tumor segmentation, and Chest X-ray Pneumonia for classification. A Concatenated CNN (CCNN) uses the improved datasets to achieve a Dice coefficient of 99.60% (KiTS19, +2.40% over baseline), segmentation accuracy of 0.983 (KiTS19) and 0.981 (BraTS2020) versus 0.959 and 0.943 (CLAHE), and classification accuracy of 0.974 (Chest X-ray) versus 0.917 (CLAHE). A classic CNN is trained on original and CLAHE-filtered datasets. These outcomes demonstrate how well the pipeline works to improve image quality and increase segmentation/classification accuracy, offering a foundation for clinical diagnostics that is both scalable and interpretable. Full article

(This article belongs to the Special Issue Medical Imaging: Artificial Intelligence, Image Recognition, and Machine Learning Techniques (2nd Edition）)

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