Dermatological Images Research Articles

Enhanced convolutional neural network architecture optimized by improved chameleon swarm algorithm for melanoma detection using dermatological images.

Early detection and treatment of skin cancer are important for patient recovery and survival. Dermoscopy images can help clinicians for timely identification of cancer, but manual diagnosis is time-consuming, costly, and prone to human error. To conduct this, an innovative deep learning-based approach has been proposed for automatic melanoma detection. The proposed method involves preprocessing dermoscopy images to remove artifacts, enhance contrast, and cancel noise, followed by feeding them into an optimized Convolutional Neural Network (CNN). The CNN is trained using an innovative metaheuristic called the Improved Chameleon Swarm Algorithm (CSA) to optimize its performance. The approach has been validated using the SIIM-ISIC Melanoma dataset and the results have been confirmed through rigorous evaluation metrics. Simulation results demonstrate the efficacy of the proposed method in accurately diagnosing melanoma from dermoscopy images by highlighting its potential as a valuable tool for clinicians in early cancer detection.

A Multiclassification Model for Skin Diseases Using Dermatoscopy Images with Inception-v2

Skin cancer represents a significant global public health concern, with over five million new cases diagnosed annually. If not diagnosed at an early stage, skin diseases have the potential to pose a significant threat to human life. In recent years, deep learning has increasingly been used in dermatological diagnosis. In this paper, a multiclassification model based on the Inception-v2 network and the focal loss function is proposed on the basis of deep learning, and the ISIC 2019 dataset is optimised using data augmentation and hair removal to achieve seven classifications of dermatological images and generate heat maps to visualise the predictions of the model. The results show that the model has an average accuracy of 89.04%, a precision of 87.37%, recall of 90.15%, and an F1-score of 88.76%, The accuracy rates of ResNext101, MobileNetv2, Vgg19, and ConvNet are 88.50%, 85.30%, 88.57%, and 86.90%, respectively. These results show that our proposed model performs better than the above models and performs well in classifying dermatological images, which has significant application value.

Recent Progress and Current Status of Artificial Intelligence in Skin Cancer Diagnosis: A Systematic Review—Where do we Stand?

Introduction: Skin cancer is one of the most prevalent forms worldwide, with a significant increase in recent decades. Real-time and accurate detection can reduce the burdens of invasive treatments. The advent of Artificial Intelligence (AI) and Machine learning (ML) has introduced multiple tools to aid accurate and early detection, categorizing dermatological images and proving especially valuable in regions with a shortage of specialists. However, the adoption of these AI-based tools requires consideration of efficacy, safety, and ethical implications. Objective: The systematic review aims to evaluate existing research on the detection, categorization, and assessment of skin cancer images. Methods: The systematic literature review is conducted based on studies published from 2018 to 2023 in PubMed, Scopus, Embase, Web of Science, IEEE Xplore, ACM DL, and Ovid MEDLINE. Study selection, data extraction, and inclusion are carried out after a proper evaluation of the studies. Results are presented in tables and figures using a narrative synthesis. Results: The search identified 687 studies from the database. However, after three phases of identification, screening, and evaluation, only 16 studies were chosen, focusing on developing and validating AI tools to detect, diagnose, and categorize skin cancer. This systematic review covers the selected studies in multiple dimensions. Conclusion: The use of AI and ML in dermatology has revolutionized the early detection of cancer, but it is necessary to validate and collaborate with healthcare professionals to ensure efficacy, safety, and effectiveness.

Blind Separation of Skin Chromophores from Multispectral Dermatological Images.

Background/Objectives: Based on Blind Source Separation and the use of multispectral imaging, the new approach we propose in this paper aims to improve the estimation of the concentrations of the main skin chromophores (melanin, oxyhemoglobin and deoxyhemoglobin), while considering shading as a fully-fledged source. Methods: In this paper, we demonstrate that the use of the Infra-Red spectral band, in addition to the traditional RGB spectral bands of dermatological images, allows us to model the image provided by each spectral band as a mixture of the concentrations of the three chromophores in addition to that of the shading, which are estimated through four steps using Blind Source Separation. Results: We studied the performance of our new method on a database of real multispectral dermatological images of melanoma by proposing a new quantitative performances measurement criterion based on mutual information. We then validated these performances on a database of multispectral dermatological images that we simulated using our own new protocol. Conclusions: All the results obtained demonstrated the effectiveness of our new approach for estimating the concentrations of the skin chromophores from a multispectral dermatological image, compared to traditional approaches that consist of using only the RGB image by neglecting shading.

The suitability of AI in dermatology for enhanced skin care

This piece highlights the tremendous potential of Artificial Intelligence (AI) in the field of dermatology and its suitability in revolutionising patient care. The integration of AI technologies into dermatological practices has the power to significantly improve diagnostics, treatment decisions, and overall patient outcomes. AI algorithms have shown remarkable proficiency in analysing dermatological images with impressive accuracy, such as skin lesions, rashes and moles. By leveraging deep learning and computer vision techniques, AI models can recognise patterns, features, and characteristics of various skin conditions, thereby aiding in accurate diagnosis and assists dermatologists in formulating personalised treatment plans tailored to individual patients.

An Efficient Algorithm for Automated Skin Lesion Detection: A Non-Machine Learning Approach

Skin lesion detection is a very important task in medical applications such as disease diagnostic and cancer detection. This paper suggests an efficient algorithm for skin lesion detection relying on advanced image processing techniques without using machine learning or deep learning algorithms. A set of algorithms is proposed to enhance traditional image processing methods to scrutinize dermatological images, concentrating on melanoma detection. The proposed method combines color space transformation, contrast enhancement, active contour segmentation. ABCD criteria (Asymmetry, Border irregularity, Color variegation, and Diameter) is used in feature extraction. The approach is evaluated on the ISIC (International Skin Image Collaboration) 2020 dataset, accomplishing an accuracy of 84% in differentiating between malignant and benign skin lesions. The results indicate the potential of non-machine learning techniques in dermatological image analysis, providing computationally efficient and interpretable alternative to data-intensive deep learning algorithms.

Interobserver agreement on line-field confocal optical coherence tomography image markers in keratinocyte carcinomas and precursor lesions

Line-field confocal optical coherence tomography (LC-OCT) is a new technology for skin cancer diagnostics. However, the interobserver agreement (IOA) of known image markers of keratinocyte carcinomas (KC), including basal cell carcinoma (BCC) and squamous cell carcinoma (SCC), as well as precursors, SCC in situ (CIS) and actinic keratosis (AK), remains unexplored. This study determined IOA on the presence or absence of 10 key LC-OCT image markers of KC and precursors, among evaluators new to LC-OCT with different levels of dermatologic imaging experience. Secondly, the frequency and association between reported image markers and lesion types, was determined. Six evaluators blinded to histopathologic diagnoses, assessed 75 LC-OCT images of KC (21 SCC; 21 BCC), CIS (12), and AK (21). For each image, evaluators independently reported the presence or absence of 10 predefined key image markers of KCs and precursors described in an LC-OCT literature review. Evaluators were stratified by experience-level as experienced (3) or novices (3) based on previous OCT and reflectance confocal microscopy usage. IOA was tested for all groups, using Conger’s kappa coefficient (κ). The frequency of reported image marker and their association with lesion-types, were calculated as proportions and odds ratios (OR), respectively. Overall IOA was highest for the image markers lobules (κ = 0.68, 95% confidence interval (CI) 0.57;0.78) and clefting (κ = 0.63, CI 0.52;0.74), typically seen in BCC (94%;OR 143.2 and 158.7, respectively, p < 0.001), followed by severe dysplasia (κ = 0.42, CI 0.31;0.53), observed primarily in CIS (79%;OR 7.1, p < 0.001). The remaining seven image-markers had lower IOA (κ = 0.06–0.32) and were more evenly observed across lesion types. The lowest IOA was noted for a well-defined (κ = 0.07, CI 0;0.15) and interrupted dermal-epidermal junction (DEJ) (κ = 0.06, CI -0.002;0.13). IOA was higher for all image markers among experienced evaluators versus novices. This study shows varying IOA for 10 key image markers of KC and precursors in LC-OCT images among evaluators new to the technology. IOA was highest for the assessments of lobules, clefting, and severe dysplasia while lowest for the assessment of the DEJ integrity.

DM-AHR: A Self-Supervised Conditional Diffusion Model for AI-Generated Hairless Imaging for Enhanced Skin Diagnosis Applications.

Accurate skin diagnosis through end-user applications is important for early detection and cure of severe skin diseases. However, the low quality of dermoscopic images hampers this mission, especially with the presence of hair on these kinds of images. This paper introduces DM-AHR, a novel, self-supervised conditional diffusion model designed specifically for the automatic generation of hairless dermoscopic images to improve the quality of skin diagnosis applications. The current research contributes in three significant ways to the field of dermatologic imaging. First, we develop a customized diffusion model that adeptly differentiates between hair and skin features. Second, we pioneer a novel self-supervised learning strategy that is specifically tailored to optimize performance for hairless imaging. Third, we introduce a new dataset, named DERMAHAIR (DERMatologic Automatic HAIR Removal Dataset), that is designed to advance and benchmark research in this specialized domain. These contributions significantly enhance the clarity of dermoscopic images, improving the accuracy of skin diagnosis procedures. We elaborate on the architecture of DM-AHR and demonstrate its effective performance in removing hair while preserving critical details of skin lesions. Our results show an enhancement in the accuracy of skin lesion analysis when compared to existing techniques. Given its robust performance, DM-AHR holds considerable promise for broader application in medical image enhancement.

Optimizing CNN Kernel Sizes for Enhanced Melanoma Lesion Classification in Dermoscopy Images

Skin cancer, particularly melanoma, presents a significant global health challenge due to its increasing incidence and mortality rates. Current diagnostic methods relying on visual inspection and histopathological examination are subjective and time-consuming, often leading to delayed diagnoses. Recent advancements in machine and deep learning, particularly convolutional neural networks (CNNs), offer a promising avenue for transforming melanoma detection by automating precise classification of dermoscopy images. This study leverages a comprehensive dataset sourced from Kaggle, comprising 10,605 images categorized into benign and malignant classes. Methodologically, a custom CNN architecture is trained and evaluated using varying kernel sizes (3x3, 5x5, 7x7) to optimize melanoma lesion classification. Results demonstrate that smaller kernel sizes, notably 3x3, consistently yield superior accuracy of 93.00% and F1-scores of 96.00%, indicating their efficacy in distinguishing between benign and malignant lesions. The CNN model exhibits robust generalization capabilities with minimal overfitting, supported by high validation accuracy throughout training epochs. Comparative analysis with related studies highlights competitive performance, suggesting potential enhancements through advanced feature selection and optimization techniques. Despite these advancements, challenges such as dataset diversity and model optimization persist, particularly concerning underrepresented darker skin tones. The study underscores the transformative potential of CNNs in enhancing diagnostic accuracy and efficiency in dermatological practice, paving the way for improved patient outcomes through early detection and intervention strategies. Future research directions include refining segmentation techniques and expanding dataset evaluations to ensure the model&amp;apos;s applicability across diverse clinical settings. Ultimately, this research contributes to advancing melanoma diagnosis by integrating cutting-edge deep learning methodologies with clinical practice, thereby addressing current limitations and driving forward innovations in dermatological image analysis.

Visual Learning Equity Initiative Enhances Preclinical Dermatologic Image Representation: A Cross-Sectional Study.

Despite growing diversity in the United States population, studies show that medical education lacks representation of conditions in darker skin tones. Given that medical conditions present differently in different skin tones, limited exposure to images of darker tones in medical training may contribute to incorrect or delayed diagnoses, perpetuating health inequities. This study examines the preclinical curriculum at the Georgetown University School of Medicine (GUSOM) to report on its image representation with respect to skin tone and to assess the impact of a student-driven initiative in achieving visual learning equity (VLE). Of 1050 preclinical images, 58.2% depicted conditions in light/white skin tones, 31.3% in medium/brown, and 10.5% in dark/black. The microbiology and pathology courses had the highest percentages of dark/black and medium/brown images. Infectious disease images made up 36.3% of all images with 54.6% light/white, 31.5% medium/brown, and 13.9% dark/black. Overall, the first images representing conditions were 63.5% light/white, 30.0% medium/brown, and 6.6% dark/black. When dark/black images were presented first, 64.3% were of infectious diseases, compared to 35.1% for medium/brown and only 28.4% for white/light first images that were infectious diseases. A significant increase in images of conditions in darker skin tones was observed in the IRD course 2022 compared to the IRD course 2020 (P&lt;.001). Our study highlights an underrepresentation of darker skin tones compared to lighter skin tones in the GUSOM preclinical curriculum. A student-led initiative significantly increased the representation of darker skin tones in dermatologic images, demonstrating the potential impact of such efforts in achieving VLE in medical education.J Drugs Dermatol. 2024;23(7):519-524.&nbsp; doi:10.36849/JDD.7992.

Revolutionizing teledermatology: Exploring the integration of artificial intelligence, including Generative Pre-trained Transformer chatbots for artificial intelligence-driven anamnesis, diagnosis, and treatment plans

The integration of teledermatology and artificial intelligence (AI) marks a significant advancement in dermatologic care. This study examines the synergistic interplay between these two domains, highlighting their collective impact on enhancing the accuracy, accessibility, and efficiency of teledermatologic services. Teledermatology expands dermatologic care to remote and underserved areas, and AI technologies show considerable potential in analyzing dermatologic images and performing various tasks involved in teledermatology consultations. Such integration facilitates rapid, precise diagnoses, personalized treatment plans, and data-driven insights. Our explorative study involved designing a GPT-based chatbot named “Dr. DermBot” and exploring its performance in a teledermatologic consultation process. The design phase focused on the chatbot's ability to conduct consultations autonomously. The subsequent testing phase assessed its performance against the backdrop of current teledermatologic practices, exploring the potential of AI and chatbots to simulate and potentially enhance teledermatologic health care. Our study demonstrates the promising future of combining teledermatology with AI. It also brings to light ethical and legal concerns, including the protection of patient data privacy and adherence to regulatory standards. The union of teledermatology and AI not only aims to enhance the precision of teledermatologic diagnoses but also broadens the accessibility of dermatologic services to previously underserved populations, benefiting patients, health care providers, and the overall health care system.

High‐resolution ultrasound in the current landscape of dermatology imaging

High‐resolution ultrasound in the current landscape of dermatology imaging

Bluish veil detection and lesion classification using custom deep learnable layers with explainable artificial intelligence (XAI)

Melanoma, one of the deadliest types of skin cancer, accounts for thousands of fatalities globally. The bluish, blue-whitish, or blue-white veil (BWV) is a critical feature for diagnosing melanoma, yet research into detecting BWV in dermatological images is limited. This study utilizes a non-annotated skin lesion dataset, which is converted into an annotated dataset using a proposed imaging algorithm (color threshold techniques) on lesion patches based on color palettes. A Deep Convolutional Neural Network (DCNN) is designed and trained separately on three individual and combined dermoscopic datasets, using custom layers instead of standard activation function layers. The model is developed to categorize skin lesions based on the presence of BWV. The proposed DCNN demonstrates superior performance compared to the conventional BWV detection models across different datasets. The model achieves a testing accuracy of 85.71 % on the augmented PH2 dataset, 95.00 % on the augmented ISIC archive dataset, 95.05 % on the combined augmented (PH2+ISIC archive) dataset, and 90.00 % on the Derm7pt dataset. An explainable artificial intelligence (XAI) algorithm is subsequently applied to interpret the DCNN's decision-making process about the BWV detection. The proposed approach, coupled with XAI, significantly improves the detection of BWV in skin lesions, outperforming existing models and providing a robust tool for early melanoma diagnosis.

Deep Learning Algorithms for Skin Disease Classification

Skin diseases are a serious concern of public health worldwide, and successful treatment needs a correct and timely diagnosis. Traditional diagnostic methods mostly depend on dermatologist’s visual observation and this leads to subjective interpretations coupled with time-consuming processes. Deep learning algorithms have lately been known as powerful means for automated medical image analysis that present more accurate and quicker results at the same time. This study analyses the usage of state-of-the-art deep learning algorithms like YOLOv8, Deep CNN, and ResNet50 used for classification of skin diseases using dermatological images. Classifying the skin conditions relies heavily on the ability to identify and extract essential features. Different skin conditions were covered under large dataset thus providing a comprehensive foundation for training and validation aimed at ensuring that the models could generalize well across different diseases. Each algorithm also employs transfer learning techniques by utilizing pre-trained models based on large image datasets in order to improve adaptability and generalization over new data types. The use of deep learning algorithms in classifying skin diseases represents a significant method to achieve efficient and accurate diagnosis with benefits to both patients and healthcare professionals as is the trend in medical image analysis. The advanced deep learning models introduced in this paper excel at classifying complex skin diseases, outperforming the machine learning approaches in performance.

Joint-individual fusion structure with fusion attention module for multi-modal skin cancer classification

Many convolutional neural network (CNN) based approaches for skin cancer classification primarily rely on dermatological images, yielding commendable results in classification accuracy. However, leveraging patient metadata, a crucial source of clinical information for dermatologists, can further enhance accuracy. Current methodologies predominantly employ basic joint fusion structures (FS) and fusion modules (FMs) for multi-modal classification, leaving room for advancement in enhancing accuracy through exploration of more sophisticated FS and FM architectures. Thus, this paper introduces a novel fusion method that integrates dermatological images (dermoscopy images or clinical images) with patient metadata for skin cancer classification, focusing on enhancing FS and FM components. Initially, we propose a joint-individual fusion (JIF) structure that simultaneously learns shared features across multi-modality data while preserving specific characteristics. Subsequently, we introduce a multi-modal fusion attention (MMFA) module designed to amplify the most relevant image and metadata features through a combination of self and mutual attention mechanisms, thereby bolstering the decision-making pipeline. Our study compares the efficacy of the proposed JIF-MMFA method with other state-of-the-art fusion techniques across three distinct public datasets. Results demonstrate that the JIF-MMFA method consistently enhances classification outcomes across various CNN backbones, outperforming alternative fusion methodologies on all three datasets. These findings underscore the effectiveness and robustness of our proposed approach in skin cancer classification.

Enhancing Skin Cancer Classification using Efficient Net B0-B7 through Convolutional Neural Networks and Transfer Learning with Patient-Specific Data.

Skin cancer diagnosis challenges dermatologists due to its complex visual variations across diagnostic categories. Convolutional neural networks (CNNs), specifically the Efficient Net B0-B7 series, have shown superiority in multiclass skin cancer classification. This study addresses the limitations of visual examination by presenting a tailored preprocessing pipeline designed for Efficient Net models. Leveraging transfer learning with pre-trained ImageNet weights, the research aims to enhance diagnostic accuracy in an imbalanced multiclass classification context. The study develops a specialized image preprocessing pipeline involving image scaling, dataset augmentation, and artifact removal tailored to the nuances of Efficient Net models. Using the Efficient Net B0-B7 dataset, transfer learning fine-tunes CNNs with pre-trained ImageNet weights. Rigorous evaluation employs key metrics like Precision, Recall, Accuracy, F1 Score, and Confusion Matrices to assess the impact of transfer learning and fine-tuning on each Efficient Net variant's performance in classifying diverse skin cancer categories. The research showcases the effectiveness of the tailored preprocessing pipeline for Efficient Net models. Transfer learning and fine-tuning significantly enhance the models' ability to discern diverse skin cancer categories. The evaluation of eight Efficient Net models (B0-B7) for skin cancer classification reveals distinct performance patterns across various cancer classes. While the majority class, Benign Kertosis, achieves high accuracy (>87%), challenges arise in accurately classifying Eczema classes. Melanoma, despite its minority representation (2.42% of images), attains an average accuracy of 80.51% across all models. However, suboptimal performance is observed in predicting warts molluscum (90.7%) and psoriasis (84.2%) instances, highlighting the need for targeted improvements in accurately identifying specific skin cancer types. The study on skin cancer classification utilizes EfficientNets B0-B7 with transfer learning from ImageNet weights. The pinnacle performance is observed with EfficientNet-B7, achieving a groundbreaking top-1 accuracy of 84.4% and top-5 accuracy of 97.1%. Remarkably efficient, it is 8.4 times smaller than the leading CNN. Detailed per-class classification exactitudes through Confusion Matrices affirm its proficiency, signaling the potential of EfficientNets for precise dermatological image analysis.

Leprosy

Leprosy

Large Language and Vision Assistant in dermatology: agame changer or just hype?

The integration of artificial intelligence (AI) in healthcare, particularly in the field of dermatology, has experienced significant progress through the creation of advanced tools such as the Large Language and Vision Assistant (LLaVA). This comprehensive review examines whether LLaVA represents a significant breakthrough or merely a passing trend in dermatological practice. By incorporating both language and visual analysis capabilities, LLaVA aims to support enhanced diagnostic accuracy, patient engagement and customized treatment planning, as evidenced by current research and case studies. However, its practical utility in a clinical setting remains a subject of debate. We explore the visual assistant chatbot's potential in improving diagnostic precision, especially in analysing skin lesions and conditions that are visually complex. The tool's capacity to process and interpret dermatological images using advanced algorithms could aid clinicians in the early detection and management of skin diseases. Furthermore, LLaVA's interactive nature potentially improves patient education and adherence to treatment protocols. Despite these advantages, there are noteworthy limitations and risks. The accuracy of LLaVA in handling atypical or rare dermatological cases is an area of concern. The tool's reliance on existing medical data raises questions about bias and the generalizability of its findings. Additionally, ethical considerations, such as patient data privacy and the potential for overreliance on AI in clinical decision making, are critical issues that need addressing. This article aims to provide dermatologists with a comprehensive understanding of LLaVA's capabilities and limitations. We discuss practical guidelines for its integration into research and clinical educational augmentation, ensuring that dermatologists can make informed decisions about employing this technology for the enhancement of patient care and treatment outcomes. The question remains: is LLaVA a game changer in dermatology, or is it just hype? This review endeavours to answer this, establishing a foundation for knowledgeable and efficient application of visual AI chatbots in dermatology practices.

Images in dermatology

Images in dermatology

Transparent medical image AI via an image-text foundation model grounded in medical literature.

Building trustworthy and transparent image-based medical artificial intelligence (AI) systems requires the ability to interrogate data and models at all stages of the development pipeline, from training models to post-deployment monitoring. Ideally, the data and associated AI systems could be described using terms already familiar to physicians, but this requires medical datasets densely annotated with semantically meaningful concepts. In the present study, we present a foundation model approach, named MONET (medical concept retriever), which learns how to connect medical images with text and densely scores images on concept presence to enable important tasks in medical AI development and deployment such as data auditing, model auditing and model interpretation. Dermatology provides a demanding use case for the versatility of MONET, due to the heterogeneity in diseases, skin tones and imaging modalities. We trained MONET based on 105,550 dermatological images paired with natural language descriptions from a large collection of medical literature. MONET can accurately annotate concepts across dermatology images as verified by board-certified dermatologists, competitively with supervised models built on previously concept-annotated dermatology datasets of clinical images. We demonstrate how MONET enables AI transparency across the entire AI system development pipeline, from building inherently interpretable models to dataset and model auditing, including a case study dissecting the results of an AI clinical trial.