Search Results (449)

Acne vulgaris is a prevalent inflammatory skin condition that requires accurate severity assessment for effective management. As a step toward more objective and reproducible severity assessment, this study developed an optimized transfer learning-based convolutional neural network (CNN) framework for facial acne severity classification using a restricted, imbalanced, non-public facial image dataset. A total of 442 frontal facial images were collected under natural lighting conditions. Acne severity was graded by a board-certified dermatologist using the Investigator’s Global Assessment (IGA) scale and categorized into three levels. The study systematically investigated model architecture selection, hyperparameter tuning, optimizer comparison, cross-validation, and class-imbalance handling through random oversampling, Synthetic Minority Over-sampling Technique (SMOTE), and Generative Adversarial Networks (GANs). The 5-fold cross-validation experiment supported the reliability of model performance across different data partitions, achieving an accuracy of 0.51. Resampling methods showed limited overall benefit, although some methods altered class-wise prediction patterns. To further examine model behavior, Gradient-weighted Class Activation Mapping (Grad-CAM) visualization was used to provide qualitative insight into the facial regions contributing to model predictions. Although the findings are limited by dataset size and diversity, the proposed framework suggests exploratory feasibility for automated acne severity assessment. Rather than serving as an immediately deployable clinical tool, this pipeline provides a preliminary baseline framework that requires further validation using larger, more diverse datasets, particularly to address subtle visual differences between acne severity classes. Full article

Epidermolysis bullosa (EB) comprises a group of rare inherited genodermatoses characterized by fragility and blistering of the skin and mucous membranes, chronic wounding, and significant morbidity including increased risk of squamous cell carcinoma in severe subtypes. Key unmet priorities include reducing diagnostic latency, establishing objective wound monitoring, enabling early detection of malignant transformation within chronic ulcerations, and developing therapies that durably modify disease progression. Artificial intelligence (AI) encompassing machine learning (ML), and deep learning (DL) is increasingly integrated into EB research and clinical practice to address these unmet needs. This structured narrative review synthesises current evidence on AI applications in EB spanning genetic diagnostics, wound assessment, inflammatory endotyping, drug repurposing, and emerging therapeutic technologies, and integrates evidence from registered clinical trials. In genomics, DL-based splicing prediction models and variant prioritisation frameworks accelerate pathogenic variant detection and reduce diagnostic latency. In wound care, convolutional neural networks-based platforms enable automated lesion segmentation and remote monitoring, while multimodal AI models predict healing trajectories and support stratification of wounds by chronicity. Computational transcriptomic analyses have identified candidate repurposing agents by reversing pathogenic gene expression signatures in EB tissue. Emerging convergence of AI with biosensors-integrated wound dressings and three-dimensional bioprinting of genetically corrected skin substitutes represents a transformative future direction. Translational barriers include limited EB-specific training datasets, algorithmic bias across diverse skin phototypes, the interpretability deficit of DL systems, and evolving regulatory frameworks for AI as a medical device. Expansion of internationally interoperable EB disease registries with standardised wound imaging protocols is identified as the single most impactful intervention to accelerate AI adoption. A minimum endpoint set for AI-assisted EB wound assessment, incorporating wound area trajectory, wound type classification, tissue composition, and paired patient-reported pain and itch scores, is proposed to standardise outcome reporting across future studies. Full article

Background/Objectives: The early identification of skin cancer by standard RGB dermoscopy is a clinical difficulty because of the complex visual differences between impacted lesions and healthy tissue. Methods: For the biomedical challenge, a novel approach to signal processing and image reconstruction is introduced in this study, called the spectrum-aided visual enhancer (SAVE). The proposed SAVE mechanism aims at reconstructing the diagnostically relevant spectral information from the conventional RGB dermoscopic images using the principles of hyperspectral imaging (HSI) and band selection (BS). After quality control and pre-processing, the images in the ISIC2019 dataset were selected, with 865 images that contain basal cell carcinoma (BCC), seborrheic keratosis (SK), and actinic keratosis (AK) lesions. To reduce data leakage, the dataset was split into training, validation, and testing subsets of 70%, 20%, and 10%, respectively. Five supervised deep learning object detection models were trained and tested on the conventional RGB image dataset and on the SAVE-enhanced dataset. Five supervised deep learning object detection models, namely, YOLOv8, YOLOv10, YOLOv11, SSDLite, and SSD, were trained and tested on the conventional RGB image dataset and the SAVE-enhanced dataset. Additional repeated experimental assessments and statistical comparisons were also carried out to evaluate the improvement in performance. Results: The experimental results showed that the SAVE-based pre-processing always yielded better performance in terms of lesion detection than conventional RGB image processing. The SAVE framework for SSD was evaluated and compared with all other evaluated models and was found to be the most successful, with an accuracy of 96%, a precision of 97%, a recall of 96%, and an F1 score of 96%. Conclusions: The results indicate that the proposed SAVE framework could be a promising RGB-compatible spectral enhancement technique for boosting skin cancer detection and computer-aided dermatologic analysis with the aid of AI. Full article

Background: Allergic contact dermatitis (ACD) is a common inflammatory skin disease and patch testing (PT) remains the gold standard for its diagnosis; however, PT interpretation is time-consuming and prone to inter-observer variability. Growing advances in digital imaging and artificial intelligence (AI) have encouraged the development of automated PT evaluation systems. This review aimed to summarize the use of deep learning networks (DNNs) and image-preprocessing techniques for PT classification. Methods: A literature review was conducted to identify original research published between 2020 and 2025 that applied deep learning algorithms to PT image analysis. Included studies were assessed with respect to model architecture, dataset characteristics, preprocessing strategies, and diagnostic performance. Results: Six original studies employing deep learning for PT image classification met the inclusion criteria. They employed a range of architectures, including YOLOv5x, EfficientNetB0, Xception, and custom CNN models. Reported diagnostic performance varied, with accuracy values ranging from 90% to 99.5%, F1-scores from 0.37 to 0.98, and AUROC values up to 0.94. Despite promising results, models remain unreliable for ICDRG grading, especially for severe reactions, and methodological variability in dataset composition, imaging conditions, preprocessing pipelines, and classification tasks limits comparability across studies. Conclusions: Deep learning shows promise for automated PT interpretation, but further standardized and multicenter studies with detailed preprocessing protocols and comprehensive ICDRG grading are required for clinical implementation. Full article

Background/Objectives: Vision Transformers (ViTs) have demonstrated impressive performance in dealing with large-scale natural image datasets. They have started to be used in medical image classification problems as well. However, how they behave under real-world conditions, such as data scarcity and extreme class imbalance, has not been well investigated. In this study, we examine the feasibility of using a standard Vision Transformer Base model that learned from scratch how to classify skin lesion images into multiple classes using the ISIC 2019 dataset. Methods: The Vision Transformer architecture was trained from scratch using stratified splitting of the data, class-balanced cross-entropy loss, multi-seed initialization, and control of hyperparameters such as patch size and dropout rate. The evaluation of the Vision Transformer architecture was performed using a hold-out test set with metrics such as accuracy, macro-F1, weighted-F1, and analysis of the confusion matrix. Results: Across all configurations, the training exhibited substantial instability and consistent overfitting behavior, with an average accuracy gap between validation and test sets of 22.7%. Test accuracy ranged from 8.0% to 37.8%, showing high sensitivity to initialization. For minority classes, the F1-score remained very low (F1 < 0.05) even though the classes were balanced in the loss function. Conclusions: The results indicate that a standard ViT-Base model trained from scratch can exhibit pronounced instability and a tendency toward majority-class bias when applied to multi-class skin lesion classification under conditions of extreme class imbalance and data scarcity. The findings point to the limitations of using simple transformer models without pre-training or other forms of inductive bias in scarce data settings. Full article

Conventional cosmetic human studies rely on pre–post mean comparisons, which have limitations in explaining where and how facial skin changes occur. This pilot single-arm study proposed a privacy-preserving three-dimensional (3D) facial mesh mapping framework and demonstrated its application using an illustrative dataset obtained from participants who used a polydeoxyribonucleotide (PDRN)-containing cosmetic. Twenty-two participants underwent facial skin assessments before and after product use. Conventional analysis included pre–post comparisons of elasticity-related parameters. Additionally, 3D facial images obtained via stereophotogrammetry were converted into de-identified mesh surfaces, spatially aligned between time points, and visualized using color-coded heatmaps. For each participant, the left facial panel displayed changes in a skin hydration permittivity index, while the right panel displayed changes in the R2 gross elasticity parameter (Ua/Uf). Overall mean values tended to increase after product use; however, the 3D visualization revealed heterogeneous spatial patterns undetectable via mean values. This method improved spatial matching, enabled intuitive regional comparison, and reduced privacy concerns by removing identifiable facial features. The privacy-preserving 3D facial mesh mapping (P3DMM) framework may serve as a complementary tool for cosmetic human studies, enabling the generation of structured, de-identified spatial datasets for future skin response research. Full article

Skin cancer image data often exhibit long-tailed distributions due to the inherent challenges in data collection and annotation. Specifically, a few predominant classes dominate a dataset of interest, while minority classes, referred to as tail classes, are underrepresented with only limited numbers of samples. Such imbalance is highly likely to adversely affect the performance of deep learning models. To address this issue, previous methods employ mixup techniques to synthesize tail-class images, thereby attempting to balance the training data. However, traditional mixup methods typically do not specifically pay attention to specific regions of interest, blending two images with indistinction between objects of interest and background. Such disregard for important semantic features may result in synthetic samples with broken or distorted diagnostic features. In this work, we introduce a novel framework, the Tail-synthetic Learning for Boosting Long-tailed Skin Cancer Image Classification (TailBoost) framework. Our approach generates a new tail-class image by combining a tail-class image with a head-class image under the guidance of their corresponding saliency maps. This strategy, namely SPMix, preserves and enhances the discriminative features of the tail-class image with minimum interference from the head-class image. We further refine the learned representations by incorporating supervised contrastive learning with class-center rebalance. Extensive experiments on the ISIC2018, ISIC2019, and PAD-UFES-20 datasets demonstrate that TailBoost outperforms existing state-of-the-art long-tailed learning methods. Full article

Accurate and consistent forensic bruise assessment is critical in ensuring positive clinical and legal outcomes for victims of violence. In this study, a framework for automated bruise detection is presented that, for the first time, integrates narrowband alternate-light-source (ALS) forensic imaging and ambient white light imaging. This evaluation framework is designed to address long-standing issues with respect to equitable performance across skin tones and lighting scenarios via a combination of novel model diagnostic strategies. In particular, skin-tone balancing during training and testing, threshold-sensitivity analysis, and embedding-similarity partitioning are employed to quantify the model robustness and deployment trade-offs that arise in forensic image analysis. Models were implemented with ImageNet-pretrained backbones and trained on a unique, multi-annotator full-consensus dataset comprising both white-light and ALS (415 nm and 450 nm) images. The protocol emphasizes three axes of operational relevance: (1) illumination composition in training (W/ALS ratio); (2) subgroup fairness via targeted balancing; and (3) model operating-point selection (confidence and IoU thresholds) informed by confidence-stability metrics and bootstrapped uncertainty estimates. Systematic W/ALS ratio sweeps indicate peak accuracy under ALS-dominant training and declining performance as the proportion of white-light images increases within the training set. Skin-tone balancing reduced failure rates for darker skin tones but increased overprediction in some demographic subgroups. Embedding-similarity and seen/unseen injury analyses demonstrate inflated generalization under image-level partitioning. Ultimately, the findings suggest that future researchers and developers should employ injury-level data partitioning and ensure a weighted balance of ALS images during training. Full article

To address the recognition challenges caused by blurred state boundaries and the limitations of single monitoring modalities during aircraft skin laser paint stripping, this study proposes a multimodal data fusion method for state recognition based on laser-induced breakdown spectroscopy (LIBS) and surface imaging. By constructing a synchronous monitoring platform, a dataset covering five key physical states, namely topcoat (Tc), topcoat–primer transition (Tc-Pr), primer (Pr), primer–substrate transition (Pr-As), and substrate damage (As), was established. The proposed gated weighted multimodal fusion network (PGMF-Net) employs SE-ResNet1D to capture variations in elemental composition features from the spectra and integrates ResNet18 to extract changes in surface morphology from the images. The experimental results show that the proposed model outperforms the single-modal methods as well as the compared early-fusion and late-fusion methods, achieving a recognition accuracy of 94.12% on the test set and an average accuracy of 94.87% in stratified cross-validation. The bootstrap-based confidence interval analysis further verifies the stability of this method under the current dataset conditions. Further analysis indicates that the single-spectrum model has difficulty effectively distinguishing coating transition states because different transition states contain identical or highly similar characteristic peak information. The single-vision model, however, shows insufficient sensitivity to subtle substrate damage, whereas multimodal fusion enables complementary representation of material composition information and surface morphological information. Experimental validation under different power conditions further confirms that the model outputs are generally consistent with the macroscopic morphological evolution observed on the sample surface. This method compensates for the limitations of traditional single-source monitoring and provides a methodological foundation for online monitoring and state feedback during the laser paint stripping process. Full article

The diagnosis of skin cancer remains a growing challenge because of its high variability as a result of the varying imaging conditions in clinical settings. This paper proposes a multimodal deep learning framework to address these challenges by combining the auxiliary clinical information with dermoscopic image features. This proposed architecture uses an attention-based feature encoder with a structured multimodal fusion approach to utilize the integrated feature representation across all channels. Evaluations of the proposed architecture were conducted across a range of benchmark datasets, including ISIC 2019, ISIC 2020, and HAM10000, using a unified experimental approach. This proposed model achieved accuracies of 90.5%, 88.7%, and 91.8% and AUCs of 95.8%, 94.6%, and 96.3%, respectively, on the selected datasets. For the baseline models, ResNet50 and EfficientNet-B4, our approach increased the AUC by 6.5% and the F1 score by 8.0%. Furthermore, across various datasets, the model achieved an AUC of 90.9%, proposing strong generalization. From the ablation analysis results, the attention and multimodal fusion mechanisms showed a 4.1% decrease in AUC when key components were removed, confirming their effectiveness. With 34.7 million parameters and an average of 19.3 Ms., the model has adequate intensity to deploy in a real clinical setting without affecting its performance. Additionally, the improvements to the model were statistically significant across all evaluation metrics (p = 0.01). The proposed multimodal framework demonstrates strong performance and robustness across multiple benchmark datasets. Full article

In impedance imaging, the incompatibility and nonlinearity of the inverse problem lead to problems such as blurred boundaries and severe artifacts in the reconstructed images, making it difficult to meet the requirements for precise identification of multi-layer tissue structures in the legs. To this end, this paper proposes a post-processing algorithm for leg EIT that integrates the boundary attention mechanism, with a Wasserstein generative adversarial network as the training framework, cyclic residual U-Net as the generator, and the boundary attention module embedded in the RecurrentBlock. This leads to adaptive enhancement of the ability to extract organizational boundary features through a three-path fusion of spatial attention, channel attention, and learnable Laplacian edge enhancement. A leg anatomy prior constraint loss function was designed, integrating six constraints—pixel loss, edge loss, hierarchical tissue constraint, total variation regularization, structural similarity loss, and histogram matching—to guide the reconstruction results to conform to the multi-layered tissue structure features of the leg. A simulation dataset of leg sections containing multiple tissues such as skin, fat, muscle, bone, blood vessels, and nerves was constructed, and the pre-reconstructed images were obtained using the hybrid total variation regularization algorithm as the network input. The simulation results show that, under noise-free and different signal-to-noise ratio conditions, the proposed BAM-R2UNet algorithm achieves the best performance in RMSE, SSIM and PSNR metrics compared with HTV, DnCNN and standard U-Net algorithms, can remove artifacts, accurately restore the boundary and conductivity distribution of leg tissues, and has stronger anti-noise robustness. Full article

Background: Directional hemispherical reflectance (DHR) is a precise method for evaluating skin reflectance and is widely used in dermatological, photobiological, and cosmetic or pharmaceutical research. Reflectance measurements may support emissivity-related interpretation, particularly in the infrared range, being influenced by chromophore content, epidermal structure, and physiological factors such as hydration, pigmentation, and surface heterogeneity. However, most in vivo studies have focused on limited spectral ranges or selected anatomical sites. This study aimed to assess skin directional hemispherical reflectance across a broad spectral range and to provide an integrated dataset supporting emissivity-related interpretation in the infrared region. Methods: The study included 20 women aged 22–50 years (27 ± 9 years) with Fitzpatrick skin phototypes II–III. Reflectance measurements were performed at 14 anatomical sites using an ET 100 emissometer (1.9–21 µm) and an SOC 410 Solar DHR reflectometer (335–2500 nm). Infrared measurements were conducted at incidence angles of 20° and 60° to assess angular effects. Data were statistically analyzed. Results: The lowest reflectance values were observed within 335–380 nm, 1700–2500 nm, and 1.5–21 µm, whereas the highest reflectance was recorded in the 590–720 nm and 700–1100 nm bands. Reflectance symmetry between body sides was observed. In the infrared range, reflectance decreased with increasing wavelength, while mid- and far-infrared values were more uniform across locations. The highest reflectance values were noted for the thigh, calf (crural region), forearm, and palmar surface of the hand, whereas the lowest values were observed in the neck, abdominal region, and dorsal surface of the hand. Measurements at 60° incidence yielded higher reflectance values than those at 20°, while preserving spatial patterns. Conclusions: Directional hemispherical reflectance provides a robust approach for assessing skin reflectance across a broad spectral range. Reflectance depends on wavelength, anatomical location, and physiological factors, including epidermal thickness, pigmentation, and sebum presence. The integrated analysis of spectral, anatomical, and angular variability may support improved interpretation of skin optical properties and contribute to reference data for biomedical and infrared imaging applications. Full article

The rising incidence of skin cancer necessitates scalable and accurate diagnostic tools. While dermoscopy-based systems have achieved expert-level performance, clinical smartphone images pose challenges due to variability in lighting, resolution, and artifacts. Recent advances in multimodal deep learning have shown promise, yet most approaches rely on simple feature concatenation or single-model classifiers, limiting their ability to capture complex cross-modal interactions. This study aims to bridge the diagnostic gap in resource-limited settings by developing a robust multimodal framework that synergizes clinical smartphone images with structured patient metadata for automated skin cancer classification. We propose a novel hybrid architecture integrating a Swin Transformer V2 (SwinV2-Tiny) for hierarchical visual feature extraction and a Denoising Autoencoder (DAE) with PCA for robust metadata embedding. These heterogeneous modalities are fused via a Gated Attention Mechanism that dynamically weighs feature importance across streams. Classification is performed by a Heterogeneous Meta-Stack Ensemble comprising CatBoost, LightGBM, XGBoost, and Logistic Regression, designed to maximize calibration and generalization across imbalanced classes. Evaluated on the PAD-UFES-20 dataset (2298 clinical smartphone images, six diagnostic classes), the proposed framework achieves state-of-the-art performance with a macro-averaged F1-score of 0.977, accuracy of 0.978, and an AUC of 0.990. It significantly outperforms unimodal baselines and existing multimodal methods, demonstrating superior sensitivity (0.974) and precision (0.981), particularly for underrepresented malignant classes like Melanoma (F1: 0.995) and Squamous Cell Carcinoma (F1: 0.960). The integration of clinical metadata with advanced visual embeddings via gated attention significantly enhances diagnostic reliability. Comprehensive ablation studies confirm the contribution of each architectural component. This framework offers a viable pathway for deploying high-precision, AI-driven dermatological screening tools on standard smartphone devices. Full article

Background: This study aims to systematically review the current literature on the application of radiomic features and artificial intelligence (AI) in the diagnosis and prognosis of common peripheral nerve-related conditions, including carpal tunnel syndrome (CTS), chronic inflammatory demyelinating polyneuropathy (CIDP), polyneuropathy, organomegaly, endocrinopathy, monoclonal gammopathy and skin abnormalities (POEMS) syndrome, and in distinguishing between benign and malignant tumors. Methods: A comprehensive literature search was conducted in PubMed and Google Scholar for studies published between January 2019 and September 2025. Inclusion criteria comprised studies that used radiomics or AI-based radiomics approaches with diagnostic or prognostic purposes in peripheral nerve disorders. Results: A total of 40 studies were identified, of which 17 met the inclusion criteria. Among these, 9 studies employed magnetic resonance imaging (MRI), including one combined with PET/CT, while 8 used ultrasound (US). Most studies were retrospective and limited by small sample sizes, lack of external validation, and predominance of single-center designs. Conclusions: Since a seminal study published in 2019, there has been increasing evidence supporting the role of radiomics and AI in improving the diagnosis and prognosis of peripheral nerve disorders, particularly using MRI and US. However, significant challenges remain, including variability in imaging data, segmentation complexity, and limited availability of validated datasets. Future advancements in imaging technologies and multidisciplinary collaboration are essential to enhance clinical applicability. Full article

Skin cancer represents a significant public health challenge, necessitating early detection and timely treatment for optimal management. Timely and accurate evaluation of skin lesions is crucial, as delays can lead to more severe outcomes. However, identifying skin lesions accurately can be challenging due to differences in color, shape, and the various types of imaging equipment used for diagnosis. While recent studies have demonstrated the potential of ensemble convolutional neural networks (CNNs) for early diagnosis of skin disorders, these models are often too large and inefficient for processing contextual information. Although lightweight networks like MobileNetV3 and EfficientNet have been developed to reduce parameters and enable deep neural networks on mobile devices, their performance is limited by inadequate feature representation depth. To mitigate these limitations, we propose a new hybrid attention dual-stream deep learning model for skin lesion detection. Our model uses one training process to preprocess the images and splits the task into two branches. Each branch extracts different features using multi-stage and multi-branch attention techniques, improving the model’s ability to detect skin lesions accurately. The first branch processes the original image using a convolutional layer integrated with three novel attention modules: Enhanced Separable Depthwise Convolution (SCAttn), stage attention, and branch attention. The second branch utilizes Contrast Limited Adaptive Histogram Equalization (CLAHE) to enhance the input image, improving local contrast and revealing finer details. The integration of CLAHE with SCAttn modules leverages enhanced local contrast to capture more nuanced features while maintaining computational efficiency. A classification module receives the concatenated hierarchical characteristics that were taken from both branches. Utilizing the PAD2020 and ISIC 2019 datasets, we assessed the proposed model and obtained an accuracy rate of 98.59% for PAD2020, surpassing the state-of-the-art performance by 2%, and stable performance accuracy for the ISIC 2019 dataset. This illustrates how well the model can integrate several attention mechanisms and feature enhancement methods, providing a reliable and effective means of detecting skin cancer. Full article