A study of social isolation in patients with varicose veins of the lower extremities: A scoping review.

OBJECTIVES: To undertake a scoping review of the literature on social alienation in patients with lower extremity varicose veins in order to serve as a reference for future studies in the field. METHODS: With a focus on the phenomenon of social alienation in patients with varicose veins of the lower extremities, a systematic search of Chinese and English databases was carried out using the scoping review methodology as a framework. The included literature was summarized and analyzed with a time frame from database construction to June 24, 2024. RESULTS: A total of 15 publications were included, demonstrating that social alienation is a frequent occurrence in people with varicose veins of the lower extremities but has not yet received much attention. In individuals with varicose veins of the lower limbs, demographic factors, illness issues, psychological problems, and social factors are the key influences on social alienation. CONCLUSION: Social alienation is a common phenomenon that is unevenly distributed in patients with varicose veins of the lower leg and is influenced by a number of different circumstances. In order to better meet the social needs of patients, healthcare professionals should pay attention to the issue of social alienation in patients with varicose veins of the lower extremity, identify and implement intervention strategies quickly, and actively explore a new model of treatment and care for social alienation.

Comparative effectiveness of combined biologic agents versus standard therapies in the treatment of plaque psoriasis: a retrospective analysis.

Introduction: Plaque psoriasis is a persistent skin disorder that necessitates efficient management. This study investigates the therapeutic effectiveness and timeline for skin lesion resolution in plaque psoriasis patients treated with combined biologic agents compared to standard therapies. Methods: Conducted retrospectively between March 2020 and March 2023, the study included 162 patients with moderate to severe plaque psoriasis. Participants were divided into two groups: the Control Group, which received standard treatments, and the Combined Biologic Agent Group, which received additional biologic therapy with secukinumab. Participants in the Control Group received standard treatments, while those in the Combined Biologic Agent Group received standard treatments plus secukinumab. Results: The results showed that the Combined Biologic Agent Group experienced a significantly faster onset of therapeutic effects, with an average time of 3.04 ± 2.25 days compared to 6.12 ± 2.06 days in the Control Group. Additionally, skin lesion resolution occurred more rapidly in the biologic agent group (7.04 ± 2.13 days) than in the control group (14.56 ± 4.73 days). By week 24, the Psoriasis Area and Severity Index (PASI) scores demonstrated a more substantial reduction in the biologic agent group, decreasing from 26.98 ± 11.28 to 2.48 ± 3.01, whereas the control group showed a reduction from 25.82 ± 10.47 to 10.40 ± 7.63. The overall effectiveness rate was higher in the biologic agent group, with no cases of ineffectiveness, compared to a 20.99% ineffectiveness rate in the control group. Furthermore, there was no recurrence of the disease in the biologic agent group, while the control group experienced an 11.11% recurrence rate. Both groups had a similar incidence of adverse reactions, indicating that the addition of biologic agents does not significantly increase the risk of adverse events. Discussion: These findings suggest that combined biologic agent therapy offers a more effective and faster treatment option for plaque psoriasis without compromising safety. However, larger-scale clinical trials are necessary to validate these results and establish the long-term benefits and safety of this treatment approach in diverse patient populations.

Skin lesion segmentation using deep learning algorithm with ant colony optimization.

BACKGROUND: Segmentation of skin lesions remains essential in histological diagnosis and skin cancer surveillance. Recent advances in deep learning have paved the way for greater improvements in medical imaging. The Hybrid Residual Networks (ResUNet) model, supplemented with Ant Colony Optimization (ACO), represents the synergy of these improvements aimed at improving the efficiency and effectiveness of skin lesion diagnosis. OBJECTIVE: This paper seeks to evaluate the effectiveness of the Hybrid ResUNet model for skin lesion classification and assess its impact on optimizing ACO performance to bridge the gap between computational efficiency and clinical utility. METHODS: The study used a deep learning design on a complex dataset that included a variety of skin lesions. The method includes training a Hybrid ResUNet model with standard parameters and fine-tuning using ACO for hyperparameter optimization. Performance was evaluated using traditional metrics such as accuracy, dice coefficient, and Jaccard index compared with existing models such as residual network (ResNet) and U-Net. RESULTS: The proposed hybrid ResUNet model exhibited excellent classification accuracy, reflected in the noticeable improvement in all evaluated metrics. His ability to describe complex lesions was particularly outstanding, improving diagnostic accuracy. Our experimental results demonstrate that the proposed Hybrid ResUNet model outperforms existing state-of-the-art methods, achieving an accuracy of 95.8%, a Dice coefficient of 93.1%, and a Jaccard index of 87.5. CONCLUSION: The addition of ResUNet to ACO in the proposed Hybrid ResUNet model significantly improves the classification of skin lesions. This integration goes beyond traditional paradigms and demonstrates a viable strategy for deploying AI-powered tools in clinical settings. FUTURE WORK: Future investigations will focus on increasing the version's abilities by using multi-modal imaging information, experimenting with alternative optimization algorithms, and comparing real-world medical applicability. There is also a promising scope for enhancing computational performance and exploring the model's interpretability for more clinical adoption.

Evaluating the Role of Vitamin D in Alleviating Chronic Pruritus: A Meta-Analysis.

Chronic pruritus is a distressing condition that significantly impacts patients' quality of life. Recent research has increasingly focused on the potential role of vitamin D, given its immunomodulatory properties, in managing this condition. This meta-analysis seeks to systematically assess the effectiveness of vitamin D supplementation in alleviating chronic pruritus across diverse clinical contexts. We conducted an extensive search through multiple databases, covering literature up to July 2024, to identify relevant randomized controlled trials (RCTs) that evaluated the effect of vitamin D on chronic pruritus. Eligible studies were those that provided data on changes in pruritus severity, as measured by standardized tools, before and after vitamin D treatment. The data were synthesized using a random-effects model to address variability among the studies. This meta-analysis is registered with PROSPERO (registration number: CRD42024579353). The findings indicate that vitamin D supplementation is associated with a significant reduction in pruritus severity, the skin lesion area, and levels of inflammatory cytokines, including tumor necrosis factor (TNF), interleukin-6 (IL-6), and high-sensitivity C-reactive protein (hs-CRP), compared to controls. These results suggest that vitamin D could be a promising therapeutic option for chronic pruritus, though further rigorous studies are required to validate these findings and to elucidate the mechanisms involved.

A Rare Skin Lesion Presentation With Sarcoidosis.

Sarcoidosis is a multifaceted systemic disease of uncertain aetiology, pathologically characterised by non-caseating granulomas. Typical symptoms include coughing, dyspnoea, chest pain and lesions affecting the eyes or skin. Cutaneous sarcoidosis frequently accompanies the involvement of other organs, but isolated cutaneous presentations are also observed. We present a case of cutaneous sarcoidosis in a 31-year-old Indian male. The diagnosis of sarcoidosis was confirmed by a skin biopsy, which showed that there is a naked, non-caseating granuloma filling the upper and deep dermis, formed of epithelioid histiocytes and multinucleated giant cells. Treatment with the intralesional steroid triamcinolone acetonide (5 mg/mL) monthly is considered. We hope to raise doctors' awareness of the various forms of sarcoidosis, consequently improving diagnostic skills and patient treatment.

Cutaneous phaeohyphomycosis presenting as multiple ecthyma-like skin lesions caused by Curvularia lunata in a previously healthy man: A case report.

Ecthyma grangrenosum is an unusual condition, mostly related to Pseudomonas septicemia. Ecthyma-like skin lesions caused by cutaneous phaeohyphomycosis are extremely rare. Here, we report a case of a 20-year-old Thai man, previously healthy, presenting multiple ecthyma-like skin lesions in both arms and both legs for 2 months. Physical examination revealed ill-defined erythematous plaque with central necrotic crust at both arms and both legs. Tissue biopsy showed a neutrophil collection identified by GMS stain revealing septate hyphae organisms in the vascular lumen. The skin culture was positive for Curvularia lunata, while the final diagnosis was cutaneous phaeohyphomycosis caused by Curvularia lunata. He was empirically treated with amphotericin B and then voriconazole. Itraconazole was administered as a definitive regimen, resulting in complete resolution after 2 months of treatment. Cutaneous phaeohyphomycosis is also an uncommon cause of ecthyma-like lesions and should be considered for investigation when initial results do not demonstrate a bacterial etiology.

A novel Skin lesion prediction and classification technique: ViT-GradCAM.

BACKGROUND: Skin cancer is one of the highly occurring diseases in human life. Early detection and treatment are the prime and necessary points to reduce the malignancy of infections. Deep learning techniques are supplementary tools to assist clinical experts in detecting and localizing skin lesions. Vision transformers (ViT) based on image segmentation classification using multiple classes provide fairly accurate detection and are gaining more popularity due to legitimate multiclass prediction capabilities. MATERIALS AND METHODS: In this research, we propose a new ViT Gradient-Weighted Class Activation Mapping (GradCAM) based architecture named ViT-GradCAM for detecting and classifying skin lesions by spreading ratio on the lesion's surface area. The proposed system is trained and validated using a HAM 10000 dataset by studying seven skin lesions. The database comprises 10 015 dermatoscopic images of varied sizes. The data preprocessing and data augmentation techniques are applied to overcome the class imbalance issues and improve the model's performance. RESULT: The proposed algorithm is based on ViT models that classify the dermatoscopic images into seven classes with an accuracy of 97.28%, precision of 98.51, recall of 95.2%, and an F1 score of 94.6, respectively. The proposed ViT-GradCAM obtains better and more accurate detection and classification than other state-of-the-art deep learning-based skin lesion detection models. The architecture of ViT-GradCAM is extensively visualized to highlight the actual pixels in essential regions associated with skin-specific pathologies. CONCLUSION: This research proposes an alternate solution to overcome the challenges of detecting and classifying skin lesions using ViTs and GradCAM, which play a significant role in detecting and classifying skin lesions accurately rather than relying solely on deep learning models.

Skin, Heart, and CNS Involvement in Granulomatosis With Polyangiitis: A Case Report.

We discuss the case of a 60-year-old male who presented with ankle pain, a necrotic rash, and progressive weakness in both lower limbs and the right upper limb. An infectious workup of the skin lesions came back negative. Additionally, his kidney function tests indicated an acute kidney injury. This prompted investigations for vasculitis etiologies, which revealed a positive cytoplasmic antineutrophil cytoplasmic autoantibody (c-ANCA). His neurological deficits were also investigated, and imaging suggested embolic infarcts. Cardiac imaging showed valve vegetations and blood culture showed a lack of growth suggestive of a noninfective nature of these lesions. Based on all these findings, a kidney biopsy was obtained and demonstrated pauci-immune segmental vasculitis consistent with ANCA-associated glomerulonephritis. As such, the patient showed improvement with heavy pulse steroid and immunomodulator therapy. Although skin, heart, and CNS involvement have been previously reported with ANCA-associated vasculitis, it is rare, especially together, and can prove a diagnostic challenge. Therefore, it is important to consider vasculitis etiology in patients presenting similarly. In addition, this case highlights the overlapping clinical picture between infective endocarditis and vasculitis with valvular involvement, making differentiation between the two challenging.

Correlation and Clinical Significance of HBD-2 and CXCL-1/2 Levels at Skin Lesions with Psoriasis Vulgaris Severity.

OBJECTIVE: This study was performed to explore the clinical significance of the expression of human beta-defensin 2 (HBD-2) and chemokine ligand 1/2 (CXCL-1/2) in psoriasis vulgaris. METHODS: This study retrospectively included the study group (n = 160) and control group (n = 100) for analysis. The levels of inflammatory indicators, blood biochemical indicators, and immune indicators using ELISA. The psoriasis area and severity index (PASI) was used to evaluate disease severity. Levels of HBD-2, CXCL-1, CXCL-2 and CCL20 were determined by RT-PCR. The correlations of HBD-2, CXCL-1 and CXCL-2 levels with CCL20 and PASI scores were analyzed. The diagnostic value of HBD-2, CXCL-1 and CXCL-2 in psoriasis vulgaris was analyzed by ROC curve. RESULTS: HBD-2, CXCL-1 and CXCL-2 were highly expressed in the lesions of psoriasis vulgaris patients, and were positively correlated with CCL20 and PASI score. HBD-2, CXCL-1 and CXCL-2 alone or in combination had high diagnostic value for psoriasis vulgaris and severe psoriasis, and the combined diagnostic value of the three was higher than that of a single indicator. CONCLUSION: HBD-2, CXCL-1, and CXCL-2 levels are closely related to the severity of psoriasis vulgaris and can effectively diagnose the occurrence and progression of psoriasis vulgaris.

Preventive Effect of Royal Jelly and 10-HDA on Skin Damage in Diabetic Mice through Regulating Keratinocyte Wnt/ß-Catenin and Pyroptosis Pathway.

The objective of this study is to elucidate how Royal jelly (RJ) and 10-hydroxy-2-decanoic acid (10-HDA) prevents diabetic skin dysfunction by modulating the pyroptosis pathway. Type 2 diabetes models are induced by fat diet consumption and low dose of streptozotocin (STZ) in C57BL/6J mice and treated with RJ (100 mg kg-1 day-1) and 10-HDA, the major lipid component of royal jelly (100 mg kg-1 day-1) for 28 weeks. The results show that serum concentrations of glucose and triglyceride are significantly lower in the RJ group or 10-HDA than diabetes mellitus (DM) group. Compared to the control group, pyroptosis proteins, GSDMD, ASC, Caspase-1, and IL-1ß are increased in the skin of the diabetic model, accompanied by the activation of the Wnt/ß-catenin signal pathway. Further evaluations by RJ exhibit superior improvement of skin damage, repress activation of the Wnt/ß-catenin pathway, and attenuate keratinocyte pyroptosis, but 10-HDA cannot completely suppress the activation of Wnt/ß-catenin pathway and pyroptosis, which shows a relatively weak protective effect on skin damage which shows that RJ is a better effect on skin injury after DM.

Using AI to Differentiate Mpox From Common Skin Lesions in a Sexual Health Clinic: Algorithm Development and Validation Study.

BACKGROUND: The 2022 global outbreak of mpox has significantly impacted health facilities, and necessitated additional infection prevention and control measures and alterations to clinic processes. Early identification of suspected mpox cases will assist in mitigating these impacts. OBJECTIVE: We aimed to develop and evaluate an artificial intelligence (AI)-based tool to differentiate mpox lesion images from other skin lesions seen in a sexual health clinic. METHODS: We used a data set with 2200 images, that included mpox and non-mpox lesions images, collected from Melbourne Sexual Health Centre and web resources. We adopted deep learning approaches which involved 6 different deep learning architectures to train our AI models. We subsequently evaluated the performance of each model using a hold-out data set and an external validation data set to determine the optimal model for differentiating between mpox and non-mpox lesions. RESULTS: The DenseNet-121 model outperformed other models with an overall area under the receiver operating characteristic curve (AUC) of 0.928, an accuracy of 0.848, a precision of 0.942, a recall of 0.742, and an F1-score of 0.834. Implementation of a region of interest approach significantly improved the performance of all models, with the AUC for the DenseNet-121 model increasing to 0.982. This approach resulted in an increase in the correct classification of mpox images from 79% (55/70) to 94% (66/70). The effectiveness of this approach was further validated by a visual analysis with gradient-weighted class activation mapping, demonstrating a reduction in false detection within the background of lesion images. On the external validation data set, ResNet-18 and DenseNet-121 achieved the highest performance. ResNet-18 achieved an AUC of 0.990 and an accuracy of 0.947, and DenseNet-121 achieved an AUC of 0.982 and an accuracy of 0.926. CONCLUSIONS: Our study demonstrated it was possible to use an AI-based image recognition algorithm to accurately differentiate between mpox and common skin lesions. Our findings provide a foundation for future investigations aimed at refining the algorithm and establishing the place of such technology in a sexual health clinic.

Skin Type Diversity in Skin Lesion Datasets: A Review.

Purpose of review: Skin type diversity in image datasets refers to the representation of various skin types. This diversity allows for the verification of comparable performance of a trained model across different skin types. A widespread problem in datasets involving human skin is the lack of verifiable diversity in skin types, making it difficult to evaluate whether the performance of the trained models generalizes across different skin types. For example, the diversity issues in skin lesion datasets, which are used to train deep learning-based models, often result in lower accuracy for darker skin types that are typically under-represented in these datasets. Under-representation in datasets results in lower performance in deep learning models for under-represented skin types. Recent findings: This issue has been discussed in previous works; however, the reporting of skin types, and inherent diversity, have not been fully assessed. Some works report skin types but do not attempt to assess the representation of each skin type in datasets. Others, focusing on skin lesions, identify the issue but do not measure skin type diversity in the datasets examined. Summary: Effort is needed to address these shortcomings and move towards facilitating verifiable diversity. Building on previous works in skin lesion datasets, this review explores the general issue of skin type diversity by investigating and evaluating skin lesion datasets specifically. The main contributions of this work are an evaluation of publicly available skin lesion datasets and their metadata to assess the frequency and completeness of reporting of skin type and an investigation into the diversity and representation of each skin type within these datasets. Supplementary Information: The online version contains material available at 10.1007/s13671-024-00440-0.

Safety Footwear Impact on Workers' Gait and Foot Problems: A Comparative Study.

BACKGROUND: In this study, we hypothesized that safety footwear (SF) impacts gait patterns, potentially contributing to the podiatric symptoms reported by workers. The purpose of this work was to compare the gait analyses of workers wearing SF and sneakers using inertial sensors while also examining the occurrence of foot problems. METHODS: A consecutive cohort of workers from different occupational sectors who wore SF during their work shifts were prospectively assessed through a gait analysis. The gait analysis was conducted under two conditions: first, while wearing SF, and second, while wearing sneakers. In both conditions, inertial sensors were used (Wiva® MOB). Participants also underwent a podiatric physical examination to evaluate foot problems. RESULTS: This study shows that SF resulted in a worsening gait pattern compared to sneakers in both genders. The impact was particularly pronounced in female participants, resulting in a significant decline in walking speed and cadence. Discomfort was reported by 83.3% of participants, with a higher prevalence in females (46.6% vs. 36.6%). The SF group exhibited an elevated prevalence of foot problems, with no significant gender variations. It seems that foot problems are more likely to occur when a foot deformity, such as flat or cavus foot or hallux valgus, is present. CONCLUSIONS: This study suggests that SF may contribute to the reported podiatric symptoms among workers. Certain footwear characteristics, including weight, mis-fit, and inadequate design, may be factors associated with footwear discomfort and adverse gait patterns, potentially leading to increased foot problems among workers.

In vitro assessment of nanomedicines' propensity to cause palmar-plantar erythrodysesthesia: A Doxil vs. doxorubicin case study.

Palmar-plantar erythrodysesthesia (PPE), also known as hand and foot syndrome, is a condition characterized by inflammation-mediated damage to the skin on the palms and soles of the hands and feet. PPE limits the successful therapeutic applications of anticancer drugs. However, identifying this toxicity during preclinical studies is challenging due to the lack of accurate in vitro and in vivo animal-based models. Therefore, there is a need for reliable models that would allow the detection of this toxicity early during the drug development process. Herein, we describe the use of an in vitro skin explant assay to assess traditional DXR, Doxil reference listed drug (RLD) and two generic PEGylated liposomal DXR formulations for their abilities to cause inflammation and skin damage. We demonstrate that the results obtained with the in vitro skin explant assay model for traditional DXR and Doxil correlate with the clinical data.

MASDF-Net: A Multi-Attention Codec Network with Selective and Dynamic Fusion for Skin Lesion Segmentation.

Automated segmentation algorithms for dermoscopic images serve as effective tools that assist dermatologists in clinical diagnosis. While existing deep learning-based skin lesion segmentation algorithms have achieved certain success, challenges remain in accurately delineating the boundaries of lesion regions in dermoscopic images with irregular shapes, blurry edges, and occlusions by artifacts. To address these issues, a multi-attention codec network with selective and dynamic fusion (MASDF-Net) is proposed for skin lesion segmentation in this study. In this network, we use the pyramid vision transformer as the encoder to model the long-range dependencies between features, and we innovatively designed three modules to further enhance the performance of the network. Specifically, the multi-attention fusion (MAF) module allows for attention to be focused on high-level features from various perspectives, thereby capturing more global contextual information. The selective information gathering (SIG) module improves the existing skip-connection structure by eliminating the redundant information in low-level features. The multi-scale cascade fusion (MSCF) module dynamically fuses features from different levels of the decoder part, further refining the segmentation boundaries. We conducted comprehensive experiments on the ISIC 2016, ISIC 2017, ISIC 2018, and PH2 datasets. The experimental results demonstrate the superiority of our approach over existing state-of-the-art methods.

Radiomic and deep learning analysis of dermoscopic images for skin lesion pattern decoding.

This study aims to explore the efficacy of a hybrid deep learning and radiomics approach, supplemented with patient metadata, in the noninvasive dermoscopic imaging-based diagnosis of skin lesions. We analyzed dermoscopic images from the International Skin Imaging Collaboration (ISIC) dataset, spanning 2016-2020, encompassing a variety of skin lesions. Our approach integrates deep learning with a comprehensive radiomics analysis, utilizing a vast array of quantitative image features to precisely quantify skin lesion patterns. The dataset includes cases of three, four, and eight different skin lesion types. Our methodology was benchmarked against seven classification methods from the ISIC 2020 challenge and prior research using a binary decision framework. The proposed hybrid model demonstrated superior performance in distinguishing benign from malignant lesions, achieving area under the receiver operating characteristic curve (AUROC) scores of 99%, 95%, and 96%, and multiclass decoding AUROCs of 98.5%, 94.9%, and 96.4%, with sensitivities of 97.6%, 93.9%, and 96.0% and specificities of 98.4%, 96.7%, and 96.9% in the internal ISIC 2018 challenge, as well as in the external Jinan and Longhua datasets, respectively. Our findings suggest that the integration of radiomics and deep learning, utilizing dermoscopic images, effectively captures the heterogeneity and pattern expression of skin lesions.

Polar contrast attention and skip cross-channel aggregation for efficient learning in U-Net.

The performance of existing lesion semantic segmentation models has shown a steady improvement with the introduction of mechanisms like attention, skip connections, and deep supervision. However, these advancements often come at the expense of computational requirements, necessitating powerful graphics processing units with substantial video memory. Consequently, certain models may exhibit poor or non-existent performance on more affordable edge devices, such as smartphones and other point-of-care devices. To tackle this challenge, our paper introduces a lesion segmentation model with a low parameter count and minimal operations. This model incorporates polar transformations to simplify images, facilitating faster training and improved performance. We leverage the characteristics of polar images by directing the model's focus to areas most likely to contain segmentation information, achieved through the introduction of a learning-efficient polar-based contrast attention (PCA). This design utilizes Hadamard products to implement a lightweight attention mechanism without significantly increasing model parameters and complexities. Furthermore, we present a novel skip cross-channel aggregation (SC2A) approach for sharing cross-channel corrections, introducing Gaussian depthwise convolution to enhance nonlinearity. Extensive experiments on the ISIC 2018 and Kvasir datasets demonstrate that our model surpasses state-of-the-art models while maintaining only about 25K parameters. Additionally, our proposed model exhibits strong generalization to cross-domain data, as confirmed through experiments on the PH2 dataset and CVC-Polyp dataset. In addition, we evaluate the model's performance in a mobile setting against other lightweight models. Notably, our proposed model outperforms other advanced models in terms of IoU and Dice score, and running time.

Intelligent skin lesion segmentation using deformable attention Transformer U-Net with bidirectional attention mechanism in skin cancer images.

BACKGROUND: In recent years, the increasing prevalence of skin cancers, particularly malignant melanoma, has become a major concern for public health. The development of accurate automated segmentation techniques for skin lesions holds immense potential in alleviating the burden on medical professionals. It is of substantial clinical importance for the early identification and intervention of skin cancer. Nevertheless, the irregular shape, uneven color, and noise interference of the skin lesions have presented significant challenges to the precise segmentation. Therefore, it is crucial to develop a high-precision and intelligent skin lesion segmentation framework for clinical treatment. METHODS: A precision-driven segmentation model for skin cancer images is proposed based on the Transformer U-Net, called BiADATU-Net, which integrates the deformable attention Transformer and bidirectional attention blocks into the U-Net. The encoder part utilizes deformable attention Transformer with dual attention block, allowing adaptive learning of global and local features. The decoder part incorporates specifically tailored scSE attention modules within skip connection layers to capture image-specific context information for strong feature fusion. Additionally, deformable convolution is aggregated into two different attention blocks to learn irregular lesion features for high-precision prediction. RESULTS: A series of experiments are conducted on four skin cancer image datasets (i.e., ISIC2016, ISIC2017, ISIC2018, and PH2). The findings show that our model exhibits satisfactory segmentation performance, all achieving an accuracy rate of over 96%. CONCLUSION: Our experiment results validate the proposed BiADATU-Net achieves competitive performance supremacy compared to some state-of-the-art methods. It is potential and valuable in the field of skin lesion segmentation.

EAAC-Net: An Efficient Adaptive Attention and Convolution Fusion Network for Skin Lesion Segmentation.

Accurate segmentation of skin lesions in dermoscopic images is of key importance for quantitative analysis of melanoma. Although existing medical image segmentation methods significantly improve skin lesion segmentation, they still have limitations in extracting local features with global information, do not handle challenging lesions well, and usually have a large number of parameters and high computational complexity. To address these issues, this paper proposes an efficient adaptive attention and convolutional fusion network for skin lesion segmentation (EAAC-Net). We designed two parallel encoders, where the efficient adaptive attention feature extraction module (EAAM) adaptively establishes global spatial dependence and global channel dependence by constructing the adjacency matrix of the directed graph and can adaptively filter out the least relevant tokens at the coarse-grained region level, thus reducing the computational complexity of the self-attention mechanism. The efficient multiscale attention-based convolution module (EMA⋅C) utilizes multiscale attention for cross-space learning of local features extracted from the convolutional layer to enhance the representation of richly detailed local features. In addition, we designed a reverse attention feature fusion module (RAFM) to enhance the effective boundary information gradually. To validate the performance of our proposed network, we compared it with other methods on ISIC 2016, ISIC 2018, and PH2 public datasets, and the experimental results show that EAAC-Net has superior segmentation performance under commonly used evaluation metrics.

Skin cancer detection through attention guided dual autoencoder approach with extreme learning machine.

Skin cancer is a lethal disease, and its early detection plays a pivotal role in preventing its spread to other body organs and tissues. Artificial Intelligence (AI)-based automated methods can play a significant role in its early detection. This study presents an AI-based novel approach, termed 'DualAutoELM' for the effective identification of various types of skin cancers. The proposed method leverages a network of autoencoders, comprising two distinct autoencoders: the spatial autoencoder and the FFT (Fast Fourier Transform)-autoencoder. The spatial-autoencoder specializes in learning spatial features within input lesion images whereas the FFT-autoencoder learns to capture textural and distinguishing frequency patterns within transformed input skin lesion images through the reconstruction process. The use of attention modules at various levels within the encoder part of these autoencoders significantly improves their discriminative feature learning capabilities. An Extreme Learning Machine (ELM) with a single layer of feedforward is trained to classify skin malignancies using the characteristics that were recovered from the bottleneck layers of these autoencoders. The 'HAM10000' and 'ISIC-2017' are two publicly available datasets used to thoroughly assess the suggested approach. The experimental findings demonstrate the accuracy and robustness of the proposed technique, with AUC, precision, and accuracy values for the 'HAM10000' dataset being 0.98, 97.68% and 97.66%, and for the 'ISIC-2017' dataset being 0.95, 86.75% and 86.68%, respectively. This study highlights the possibility of the suggested approach for accurate detection of skin cancer.