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Applications of Biomedical Imaging and Sensing Technologies in Disease Diagnosis
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Applications of Biomedical Imaging and Sensing Technologies in Disease Diagnosis

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

Deadline for manuscript submissions: 30 November 2025 | Viewed by 7517

Special Issue Editor

Dr. Marco Marcon

E-Mail Website
Guest Editor
Dipartimento di Elettronica, Informazione e Bioingegneria (DEIB), Politecnico di Milano, Piazza Leonardo da Vinci, 32, 20133 Milan, Italy
Interests: computer vision; 3D acquisition systems; tomography; deep learning; pattern recognition
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We cordially invite you to submit your valuable research articles for consideration to our Special Issue on "Applications of Biomedical Imaging and Sensing Technologies in Disease Diagnosis". This Special Issue aims to explore the advancements and promising applications of biomedical imaging techniques and image sensors in the field of disease diagnosis.

Throughout the years, biomedical imaging has demonstrated its vast potential in enhancing our understanding of various diseases and improving patient care. This Special Issue intends to provide a platform for researchers, scientists, and medical professionals to share their findings, exchange knowledge, and engage in discussions regarding the integration of biomedical imaging in disease diagnosis.

We welcome submissions that contribute to the advancement of this field, including but not limited to topics such as the utilization of magnetic resonance imaging (MRI), computed tomography (CT), ultrasound, positron emission tomography (PET), and optical imaging techniques in disease diagnosis. Research articles focusing on the development of novel sensors and imaging methods, implementation of artificial intelligence algorithms, and the utilization of big data analytics for disease diagnosis are highly encouraged.

We kindly request that all submissions comply with the journal guidelines and include a clear methodology, results, and a discussion section. Our editorial team, consisting of distinguished experts in the field, will ensure a rigorous peer-review process.

We firmly believe that your exceptional research findings can make a significant impact on the field of disease diagnosis via biomedical imaging. Your contribution to this Special Issue will be immensely valuable in advancing scientific knowledge and fostering collaborative growth.

Thank you for considering our invitation. We eagerly anticipate your impressive research contributions.

Yours sincerely,
Dr. Marco Marcon
Guest Editor

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

  • machine learning for biomedical imaging
  • novel imaging sensors for disease diagnosis
  • medical imaging technology
  • diagnostic imaging techniques
  • radiology in disease diagnosis
  • magnetic resonance imaging (MRI)
  • computed tomography (CT)
  • positron emission tomography (PET)
  • ultrasonography
  • molecular imaging

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Published Papers (7 papers)

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Research

26 pages, 3790 KiB  
Article
An Enhanced Approach Using AGS Network for Skin Cancer Classification
by Hwanyoung Lee, Seeun Cho, Jiyoon Song, Hoyoung Kim and Youjin Shin
Sensors 2025, 25(2), 394; https://doi.org/10.3390/s25020394 - 10 Jan 2025
Viewed by 967
Abstract
Skin cancer accounts for over 40% of all cancer diagnoses worldwide. However, accurately diagnosing skin cancer remains challenging for dermatologists, as multiple types of skin cancer often appear visually similar. The diagnostic accuracy of dermatologists ranges between 62% and 80%. Although AI models [...] Read more.
Skin cancer accounts for over 40% of all cancer diagnoses worldwide. However, accurately diagnosing skin cancer remains challenging for dermatologists, as multiple types of skin cancer often appear visually similar. The diagnostic accuracy of dermatologists ranges between 62% and 80%. Although AI models have shown promise in assisting with skin cancer classification in various studies, obtaining the large-scale medical image datasets required for AI model training is not straightforward. To address this limitation, this study proposes the AGS network, designed to overcome the challenges of small datasets and enhance the performance of skin cancer classifiers. The AGS network integrates three key modules: Augmentation (A), GAN (G), and Segmentation (S). It was evaluated using eight deep learning classifiers—GoogLeNet, DenseNet201, ResNet50, MobileNet V3, EfficientNet B0, ViT, EfficientNet V2, and Swin Transformers—on the HAM10000 dataset. Five model configurations were also tested to assess the contribution of each module. The results showed that all eight classifiers demonstrated consistent performance improvements with the AGS network. In particular, EfficientNet V2 + AGS achieved the most significant performance gains over the baseline model, with an increase of +0.1808 in Accuracy and +0.1674 in F1-Score. Among all configurations, ResNet50+AGS achieved the best overall performance, with an Accuracy of 95.87% and an F1-Score of 95.73%. While most previous studies focused on single augmentation methods, this study demonstrates the effectiveness of combining multiple augmentation techniques within an integrated framework. The AGS network demonstrates how integrating diverse methods can improve the performance of skin cancer classification models. Full article
(This article belongs to the Special Issue Applications of Biomedical Imaging and Sensing Technologies in Disease Diagnosis)
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29 pages, 2915 KiB  
Article
Machine Learning Recognizes Stages of Parkinson’s Disease Using Magnetic Resonance Imaging
by Artur Chudzik
Sensors 2024, 24(24), 8152; https://doi.org/10.3390/s24248152 - 20 Dec 2024
Viewed by 1026
Abstract
Neurodegenerative diseases (NDs), such as Alzheimer’s disease (AD) and Parkinson’s disease (PD), are debilitating conditions that affect millions worldwide, and the number of cases is expected to rise significantly in the coming years. Because early detection is crucial for effective intervention strategies, this [...] Read more.
Neurodegenerative diseases (NDs), such as Alzheimer’s disease (AD) and Parkinson’s disease (PD), are debilitating conditions that affect millions worldwide, and the number of cases is expected to rise significantly in the coming years. Because early detection is crucial for effective intervention strategies, this study investigates whether the structural analysis of selected brain regions, including volumes and their spatial relationships obtained from regular T1-weighted MRI scans (N = 168, PPMI database), can model stages of PD using standard machine learning (ML) techniques. Thus, diverse ML models, including Logistic Regression, Random Forest, Support Vector Classifier, and Rough Sets, were trained and evaluated. Models used volumes, Euclidean, and Cosine distances of subcortical brain structures relative to the thalamus to differentiate among control (HC), prodromal (PR), and PD groups. Based on three separate experiments, the Logistic Regression approach was optimal, providing low feature complexity and strong predictive performance (accuracy: 85%, precision: 88%, recall: 85%) in PD-stage recognition. Using interpretable metrics, such as the volume- and centroid-based spatial distances, models achieved high diagnostic accuracy, presenting a promising framework for early-stage PD identification based on MRI scans. Full article
(This article belongs to the Special Issue Applications of Biomedical Imaging and Sensing Technologies in Disease Diagnosis)
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18 pages, 8204 KiB  
Article
Low-Quality Sensor Data-Based Semi-Supervised Learning for Medical Image Segmentation
by Hengfan Li, Xuanbo Xu, Ziheng Liu, Qingfeng Xia and Min Xia
Sensors 2024, 24(23), 7799; https://doi.org/10.3390/s24237799 - 5 Dec 2024
Viewed by 791
Abstract
Traditional medical image sensors face multiple challenges. First, these sensors typically rely on large amounts of labeled data, which are time-consuming and costly to obtain. Second, when the data volume and image size are large, traditional sensors have limited computational power, making it [...] Read more.
Traditional medical image sensors face multiple challenges. First, these sensors typically rely on large amounts of labeled data, which are time-consuming and costly to obtain. Second, when the data volume and image size are large, traditional sensors have limited computational power, making it difficult to effectively train and infer models. Additionally, traditional sensors have poor generalization ability and struggle to adapt to datasets with different modalities. This paper devises a novel framework, named LSDSL, and deploys it in the sensor. LSDSL utilizes low-quality sensor data for semi-supervised learning in medical image segmentation. in supervised learning, we devise the hard region exploration (hre) module to enhance the model’s comprehension of low-quality pixels in hard regions. in unsupervised learning, we introduce a pseudo-label sharing (ps) module, which allows low-quality pixels in one network to learn from the high-quality pixels in the other networks. our model outperforms other semi-supervised methods on the datasets of two different modalities (CT and MRI) in medical image sensors, achieving superior inference speed and segmentation accuracy. Full article
(This article belongs to the Special Issue Applications of Biomedical Imaging and Sensing Technologies in Disease Diagnosis)
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15 pages, 6265 KiB  
Article
LPC-SonoNet: A Lightweight Network Based on SonoNet and Light Pyramid Convolution for Fetal Ultrasound Standard Plane Detection
by Tianxiang Yu, Po-Hsiang Tsui, Denis Leonov, Shuicai Wu, Guangyu Bin and Zhuhuang Zhou
Sensors 2024, 24(23), 7510; https://doi.org/10.3390/s24237510 - 25 Nov 2024
Viewed by 937
Abstract
The detection of fetal ultrasound standard planes (FUSPs) is important for the diagnosis of fetal malformation and the prevention of perinatal death. As a promising deep-learning technique in FUSP detection, SonoNet’s network parameters have a large size. In this paper, we introduced a [...] Read more.
The detection of fetal ultrasound standard planes (FUSPs) is important for the diagnosis of fetal malformation and the prevention of perinatal death. As a promising deep-learning technique in FUSP detection, SonoNet’s network parameters have a large size. In this paper, we introduced a light pyramid convolution (LPC) block into SonoNet and proposed LPC-SonoNet with reduced network parameters for FUSP detection. The LPC block used pyramid convolution architecture inspired by SimSPPF from YOLOv6 and was able to extract features from various scales with a small parameter size. Using SonoNet64 as the backbone, the proposed network removed one of the convolutional blocks in SonoNet64 and replaced the others with LPC blocks. The proposed LPC-SonoNet model was trained and tested on a publicly available dataset with 12,400 ultrasound images. The dataset with six categories was further divided into nine categories. The images were randomly divided into a training set, a validation set, and a test set in a ratio of 8:1:1. Data augmentation was conducted on the training set to address the data imbalance issue. In the classification of six categories and nine categories, LPC-SonoNet obtained the accuracy of 97.0% and 91.9% on the test set, respectively, slightly higher than the accuracy of 96.60% and 91.70% by SonoNet64. Compared with SonoNet64 with 14.9 million parameters, LPC-SonoNet had a much smaller parameter size (4.3 million). This study pioneered the deep-learning classification of nine categories of FUSPs. The proposed LPC-SonoNet may be used as a lightweight network for FUSP detection. Full article
(This article belongs to the Special Issue Applications of Biomedical Imaging and Sensing Technologies in Disease Diagnosis)
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10 pages, 8355 KiB  
Communication
Triple Spectral Line Imaging of Whole-Body Human Skin: Equipment, Image Processing, and Clinical Data
by Janis Spigulis, Uldis Rubins, Edgars Kviesis-Kipge, Inga Saknite, Ilze Oshina and Egija Vasilisina
Sensors 2024, 24(22), 7348; https://doi.org/10.3390/s24227348 - 18 Nov 2024
Viewed by 894
Abstract
Multispectral imaging can provide objective quantitative data on various clinical pathologies, e.g., abnormal content of bio-substances in human skin. Performance of diagnostics increases with decreased spectral bandwidths of imaging; from this point, ultra-narrowband laser spectral line imaging is well suited for diagnostic applications. [...] Read more.
Multispectral imaging can provide objective quantitative data on various clinical pathologies, e.g., abnormal content of bio-substances in human skin. Performance of diagnostics increases with decreased spectral bandwidths of imaging; from this point, ultra-narrowband laser spectral line imaging is well suited for diagnostic applications. In this study, 40 volunteers participated in clinical validation tests of a newly developed prototype device for triple laser line whole-body skin imaging. The device comprised a vertically movable high-resolution camera coupled with a specific illumination unit—a side-emitting optical fiber spiral that emits simultaneously three RGB laser spectral lines at the wavelengths 450 nm, 520 nm, and 628 nm. The prototype’s design details, skin spectral image processing, and the obtained first clinical data are reported and discussed. Full article
(This article belongs to the Special Issue Applications of Biomedical Imaging and Sensing Technologies in Disease Diagnosis)
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17 pages, 2468 KiB  
Article
CRASA: Chili Pepper Disease Diagnosis via Image Reconstruction Using Background Removal and Generative Adversarial Serial Autoencoder
by Jongwook Si and Sungyoung Kim
Sensors 2024, 24(21), 6892; https://doi.org/10.3390/s24216892 - 27 Oct 2024
Cited by 1 | Viewed by 761
Abstract
With the recent development of smart farms, researchers are very interested in such fields. In particular, the field of disease diagnosis is the most important factor. Disease diagnosis belongs to the field of anomaly detection and aims to distinguish whether plants or fruits [...] Read more.
With the recent development of smart farms, researchers are very interested in such fields. In particular, the field of disease diagnosis is the most important factor. Disease diagnosis belongs to the field of anomaly detection and aims to distinguish whether plants or fruits are normal or abnormal. The problem can be solved by binary or multi-classification based on a Convolutional Neural Network (CNN), but it can also be solved by image reconstruction. However, due to the limitation of the performance of image generation, SOTA’s methods propose a score calculation method using a latent vector error. In this paper, we propose a network that focuses on chili peppers and proceeds with background removal through GrabCut. It shows a high performance through an image-based score calculation method. Due to the difficulty of reconstructing the input image, the difference between the input and output images is large. However, the serial autoencoder proposed in this paper uses the difference between the two fake images, instead of the actual input, as a score. We propose a method of generating meaningful images using the GAN structure and classifying three results simultaneously by one discriminator. The proposed method showed a higher performance than previous research, and image-based scores showed the best performance. Full article
(This article belongs to the Special Issue Applications of Biomedical Imaging and Sensing Technologies in Disease Diagnosis)
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16 pages, 5621 KiB  
Article
Kinect-Based Gait Analysis System Design and Concurrent Validity in Persons with Anterolateral Shoulder Pain Syndrome, Results from a Pilot Study
by Fredy Bernal, Veronique Feipel and Mauricio Plaza
Sensors 2024, 24(19), 6351; https://doi.org/10.3390/s24196351 - 30 Sep 2024
Viewed by 1060
Abstract
As part of an investigation to detect asymmetries in gait patterns in persons with shoulder injuries, the goal of the present study was to design and validate a Kinect-based motion capture system that would enable the extraction of joint kinematics curves during gait [...] Read more.
As part of an investigation to detect asymmetries in gait patterns in persons with shoulder injuries, the goal of the present study was to design and validate a Kinect-based motion capture system that would enable the extraction of joint kinematics curves during gait and to compare them with the data obtained through a commercial motion capture system. The study included eight male and two female participants, all diagnosed with anterolateral shoulder pain syndrome in their right upper extremity with a minimum 18 months of disorder evolution. The participants had an average age of 31.8 ± 9.8 years, a height of 173 ± 18 cm, and a weight of 81 ± 15 kg. The gait kinematics were sampled simultaneously with the new system and the Clinical 3DMA system. Shoulder, elbow, hip, and knee kinematics were compared between systems for the pathological and non-pathological sides using repeated measures ANOVA and 1D statistical parametric mapping. For most variables, no significant difference was found between systems. Evidence of a significant difference between the newly developed system and the commercial system was found for knee flexion–extension (p < 0.004, between 60 and 80% of the gait cycle), and for shoulder abduction–adduction. The good concurrent validity of the new Kinect-based motion analysis system found in this study opens promising perspectives for clinical motion tracking using an affordable and simple system. Full article
(This article belongs to the Special Issue Applications of Biomedical Imaging and Sensing Technologies in Disease Diagnosis)
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