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Skin cancer represents a significant global health concern, where early and precise diagnosis plays a pivotal role in improving treatment efficacy and patient survival rates. Nonetheless, the inherent visual similarities between benign and malignant lesions pose substantial challenges to accurate classification. To overcome these obstacles, this study proposes an innovative hybrid deep learning model that combines ConvNeXtV2 blocks and separable self-attention mechanisms, tailored to enhance feature extraction and optimize classification performance. The inclusion of ConvNeXtV2 blocks in the initial two stages is driven by their ability to effectively capture fine-grained local features and subtle patterns, which are critical for distinguishing between visually similar lesion types. Meanwhile, the adoption of separable self-attention in the later stages allows the model to selectively prioritize diagnostically relevant regions while minimizing computational complexity, addressing the inefficiencies often associated with traditional self-attention mechanisms. The model was comprehensively trained and validated on the ISIC 2019 dataset, which includes eight distinct skin lesion categories. Advanced methodologies such as data augmentation and transfer learning were employed to further enhance model robustness and reliability. The proposed architecture achieved exceptional performance metrics, with 93.48% accuracy, 93.24% precision, 90.70% recall, and a 91.82% F1-score, outperforming over ten Convolutional Neural Network (CNN) based and over ten Vision Transformer (ViT) based models tested under comparable conditions. Despite its robust performance, the model maintains a compact design with only 21.92 million parameters, making it highly efficient and suitable for model deployment. The Proposed Model demonstrates exceptional accuracy and generalizability across diverse skin lesion classes, establishing a reliable framework for early and accurate skin cancer diagnosis in clinical practice., Competing Interests: Declarations. Competing interests: The authors declare no competing interests. Consent to participate: No formal consent to participate was required for this work as it did not involve interactions with human subjects or the collection of sensitive personal information. Consent to publish: This study did not use individual person’s data. Ethics approval: No ethics approval was required for this work as it did not involve human subjects, animals, or sensitive data that would necessitate ethical review., (© 2025. The Author(s).)

Skin cancer is recognized as one of the most perilous diseases globally. In the field of medical image classification, precise identification of early-stage skin lesions is imperative for accurate diagnosis. However, deploying these algorithms on low-cost devices and attaining high-efficiency operation with minimal energy consumption poses a formidable challenge due to their intricate computational demands. This study proposes a lightweight hardware design based on a convolutional neural network (CNN) for real-time processing of skin disease classifiers on portable devices. Our skin cancer recognition processor utilizes an optimally parallel designed processing engine (PE) for global computation, which greatly reduces hardware resource utilization by multiplexing of computational unit circuits. In addition, a design approach that provides loop unrolling effectively reduces the number of data accesses, thereby reducing computational complexity and logic resource requirements. The hardware circuits in this design perform data inference in convolutional, pooling, and fully connected layers based on 16-bit floating-point numbers. Evaluation of the HAM10000 database dataset shows that the architecture achieves an average classification accuracy of 97.8 %. We are the first to implement an all-hardware FPGA-based skin cancer detection platform that offers a 3.5x speedup in recognition compared to existing skin cancer accelerators at 50 MHz while consuming only 0.48 W of power. The implementation of this hardware architecture meets the major constraints of portable devices, featuring low resource utilization, low power consumption, and cost-effectiveness, while still providing efficient classification and high accuracy results., Competing Interests: Declaration of competing interest None Declared., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Skin cancer is one of the most frequently occurring cancers worldwide, and early detection is crucial for effective treatment. Dermatologists often face challenges such as heavy data demands, potential human errors, and strict time limits, which can negatively affect diagnostic outcomes. Deep learning-based diagnostic systems offer quick, accurate testing and enhanced research capabilities, providing significant support to dermatologists. In this study, we enhanced the Swin Transformer architecture by implementing the hybrid shifted window-based multi-head self-attention (HSW-MSA) in place of the conventional shifted window-based multi-head self-attention (SW-MSA). This adjustment enables the model to more efficiently process areas of skin cancer overlap, capture finer details, and manage long-range dependencies, while maintaining memory usage and computational efficiency during training. Additionally, the study replaces the standard multi-layer perceptron (MLP) in the Swin Transformer with a SwiGLU-based MLP, an upgraded version of the gated linear unit (GLU) module, to achieve higher accuracy, faster training speeds, and better parameter efficiency. The modified Swin model-base was evaluated using the publicly accessible ISIC 2019 skin dataset with eight classes and was compared against popular convolutional neural networks (CNNs) and cutting-edge vision transformer (ViT) models. In an exhaustive assessment on the unseen test dataset, the proposed Swin-Base model demonstrated exceptional performance, achieving an accuracy of 89.36%, a recall of 85.13%, a precision of 88.22%, and an F1-score of 86.65%, surpassing all previously reported research and deep learning models documented in the literature., Competing Interests: Declarations. Ethics Approval: No ethics approval was required for this work as it did not involve human subjects, animals, or sensitive data that would necessitate ethical review. Consent to Participate: No formal consent to participate was required for this work as it did not involve interactions with human subjects or the collection of sensitive personal information. Consent for Publication: This study did not use individual person’s data. Competing Interests: The authors declare no competing interests., (© 2024. The Author(s).)

Cutaneous melanoma is the most lethal skin cancer and noninvasively distinguishing it from benign tumor is a major challenge. Raman spectroscopic measurements were conducted on 65 suspected melanocytic lesions and surrounding healthy skin from 47 patients. Compared to the spectra of healthy skin, spectra of melanocytic lesions exhibited lower intensities in carotenoid bands and higher intensities in lipid and melanin bands, suggesting similar variations in the content of these components. Distinct variations were observed among the autofluorescence intensities of healthy skin, benign nevi and malignant melanoma. By incorporating autofluorescence information, the classification accuracy of the support vector machine for spectra of healthy skin, nevi, and melanoma reached 90.2%, surpassing the 87.9% accuracy achieved without autofluorescence, with this difference being statistically significant. These findings indicate the diagnostic value of autofluorescence intensity, which reflect differences in fluorophore content, chemical composition, and structure among healthy skin, nevi, and melanoma., (© 2024 The Author(s). Journal of Biophotonics published by Wiley‐VCH GmbH.)

Skin Cancer is caused due to the mutational differences in epidermis hormones and patch appearances. Many studies are focused on the design and development of effective approaches in diagnosis and categorization of skin cancer. The decisions are made on independent training dataset under limited editions and scenarios. In this research, the kaggle based datasets are optimized and categorized into a labeled data array towards indexing using Federated learning (FL). The technique is developed on grey wolf optimization algorithm to assure the dataset attribute dependencies are extracted and dimensional mapping is processed. The threshold value validation of the dimensional mapping datasets is effectively optimized and trained under the neural networking framework further expanded via federated learning standards. The technique has demonstrated 95.82% accuracy under GWO technique and 94.9% on inter-combination of Trained Neural Networking (TNN) framework and Recessive Learning (RL) in accuracy., (© 2024. The Author(s).)

Skin cancer, including the highly lethal malignant melanoma, poses a significant global health challenge with a rising incidence rate. Early detection plays a pivotal role in improving survival rates. This study aims to develop an advanced deep learning-based approach for accurate skin lesion classification, addressing challenges such as limited data availability, class imbalance, and noise. Modern deep neural network designs, such as ResNeXt101, SeResNeXt101, ResNet152V2, DenseNet201, GoogLeNet, and Xception, which are used in the study and ze optimised using the SGD technique. The dataset comprises diverse skin lesion images from the HAM10000 and ISIC datasets. Noise and artifacts are tackled using image inpainting, and data augmentation techniques enhance training sample diversity. The ensemble technique is utilized, creating both average and weighted average ensemble models. Grid search optimizes model weight distribution. The individual models exhibit varying performance, with metrics including recall, precision, F1 score, and MCC. The "Average ensemble model" achieves harmonious balance, emphasizing precision, F1 score, and recall, yielding high performance. The "Weighted ensemble model" capitalizes on individual models' strengths, showcasing heightened precision and MCC, yielding outstanding performance. The ensemble models consistently outperform individual models, with the average ensemble model attaining a macro-average ROC-AUC score of 96 % and the weighted ensemble model achieving a macro-average ROC-AUC score of 97 %. This research demonstrates the efficacy of ensemble techniques in significantly improving skin lesion classification accuracy. By harnessing the strengths of individual models and addressing their limitations, the ensemble models exhibit robust and reliable performance across various metrics. The findings underscore the potential of ensemble techniques in enhancing medical diagnostics and contributing to improved patient outcomes in skin lesion diagnosis., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Inc.)

Background: More and more genetic and metabolic abnormalities are now known to cause cancer, which is typically deadly. Any bodily part may become infected by cancerous cells, which can be fatal. Skin cancer is one of the most prevalent types of cancer, and its prevalence is rising across the globe. Squamous and basal cell carcinomas, as well as melanoma, which is clinically aggressive and causes the majority of deaths, are the primary subtypes of skin cancer. Screening for skin cancer is therefore essential., Methods: The best way to quickly and precisely detect skin cancer is by using deep learning techniques. In this research deep learning techniques like MobileNetv2 and Dense net will be used for detecting or identifying two main kinds of tumors malignant and benign. For this research HAM10000 dataset is considered. This dataset consists of 10,000 skin lesion images and the disease comprises nonmelanocytic and melanocytic tumors. These two techniques can be used for detecting the malignant and benign. All these methods are compared and then a result can be inferred from their performance., Results: After the model evaluation, the accuracy for the MobileNetV2 was 85% and customized CNN was 95%. A web application has been developed with the Python framework that provides a graphical user interface with the best-trained model. The graphical user interface allows the user to enter the patient details and upload the lesion image. The image will be classified with the appropriate trained model which can predict whether the uploaded image is cancerous or non-cancerous. This web application also displays the percentage of cancer affected., Conclusion: As per the comparisons between the two techniques customized CNN gives higher accuracy for the detection of melanoma., (© 2024. The Author(s).)

Skin cancer is one of the most common types of malignancy, affecting a large population and causing a heavy economic burden worldwide. Over the last few years, computer-aided diagnosis has been rapidly developed and make great progress in healthcare and medical practices due to the advances in artificial intelligence, particularly with the adoption of convolutional neural networks. However, most studies in skin cancer detection keep pursuing high prediction accuracies without considering the limitation of computing resources on portable devices. In this case, the knowledge distillation (KD) method has been proven as an efficient tool to help improve the adaptability of lightweight models under limited resources, meanwhile keeping a high-level representation capability. To bridge the gap, this study specifically proposes a novel method, termed SSD-KD, that unifies diverse knowledge into a generic KD framework for skin disease classification. Our method models an intra-instance relational feature representation and integrates it with existing KD research. A dual relational knowledge distillation architecture is self-supervised trained while the weighted softened outputs are also exploited to enable the student model to capture richer knowledge from the teacher model. To demonstrate the effectiveness of our method, we conduct experiments on ISIC 2019, a large-scale open-accessed benchmark of skin diseases dermoscopic images. Experiments show that our distilled MobileNetV2 can achieve an accuracy as high as 85% for the classification tasks of 8 different skin diseases with minimal parameters and computing requirements. Ablation studies confirm the effectiveness of our intra- and inter-instance relational knowledge integration strategy. Compared with state-of-the-art knowledge distillation techniques, the proposed method demonstrates improved performance. To the best of our knowledge, this is the first deep knowledge distillation application for multi-disease classification on the large-scale dermoscopy database. Our codes and models are available at https://github.com/enkiwang/Portable-Skin-Lesion-Diagnosis., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Background: In areas without convenient access to dermatology care, primary care providers (PCPs) serve as an important patient resource for early skin cancer detection. To determine the most effective strategy for skin cancer detection training in PCPs, we conducted a systematic review of educational interventions and performed a meta-analysis on sensitivity and specificity outcomes in PCPs., Objectives: To summarize data on skin cancer sensitivity and specificity outcomes for PCP-targeted training programs and diagnostic algorithms. Our PCP cohort included practicing physicians, trainee physicians, and advanced practice practitioners., Methods: A literature search was performed in MEDLINE, Embase, Web of Science, and the Cochrane Library for relevant English-language articles published worldwide from 2000 onward. Results were screened for eligibility, and overlapping datasets were reconciled. Data extracted included the educational intervention, diagnostic algorithm, and outcomes of interest (sensitivity and specificity). Outcomes were pooled across interventions that taught the same diagnostic algorithm. A bivariate model was fit to compare different interventions/algorithms. This review followed the PRISMA guidelines., Results: In total, 21 articles were included in this review, encompassing over 58,610 assessments of skin lesions by about 1529 participants worldwide. Training programs that implemented the triage-amalgamated dermoscopic algorithm (TADA) demonstrated high pooled sensitivity (91.7%) and high pooled specificity (81.4%) among PCPs., Conclusions and Relevance: Overall, this systematic review and meta-analysis showed that dermoscopy training in PCPs was generally associated with gains in skin cancer sensitivity without loss of specificity. Clinically, this correlates with fewer skin cancers overlooked by PCPs and fewer excisions of benign lesions., (© 2022. The Author(s) under exclusive licence to American Association for Cancer Education.)

Skin cancer is one of the dangerous types of cancer and the rate of death is increasing due to the lack of knowledge in prevention and the symptoms. It is a common cancer type around the world and it occurs when the skin cells are damaged. Hence, the detection of skin cancer near the beginning is important to prevent the spread of cancer and to increase the survival rate. Recently, image processing and machine learning techniques gained more interest in medical applications. However, early analysis of skin cancer images is very challenging due to factors, like variations in the color illumination, light reflections from the skin surface, and different sizes and shapes of lesions. To detect skin cancer at an early stage and to increase the survival rate, an effective skin cancer detection method is introduced in this study using the proposed Fractional Student Psychology Based Optimization-based Deep Q Network (FSPBO-based DQN) in the wireless network scenario. At first, the nodes simulated in the network area are allowed to capture the healthcare information to make the detection strategy using the proposed method. Then, the routing is performed by the proposed Fractional Student Psychology Based Optimization (FSPBO) algorithm by considering the fitness parameters, like distance, energy, trust, and delay. After the images (healthcare information) are reached the Base Station (BS), the pre-processing, segmentation, and cancer detection processes are carried out to detect the skin lesions. Initially, the image is fed to pre-processing phase, where a Type II Fuzzy System and cuckoo search optimization algorithm (T2FCS) filter is employed to remove the noise of images. Then, the pre-processed images are fed to the segmentation phase, where speech enhancement Generative Adversarial Network (SeGAN) is used to generate the segmented results. Afterward, the Deep Q Network (DQN) detects the skin cancer based on the segmented results, and the training of DQN is made using the proposed FSPBO algorithm, which is designed by integrating the Student Psychology Based Optimization (SPBO) and Fractional Calculus (FC). The proposed method is more robust and reduces computation time and complexity. Moreover, the proposed method achieved higher performance by considering the measures, namely accuracy, sensitivity, and specificity with the values of 92.364%, 93.20%, and 92.63%., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Dielectric properties of biological tissues are traditionally measured with open-ended coaxial probes. Despite being commercially available for laboratory use, the technique suffers from high measurement error. This prevents the practical applications of the open-ended coaxial probes. One such application is the utilization of the technique for skin cancer detection. To enable a diagnostic tool, there is a need to address the error sources. Among others, tissue heterogeneity is a major contributor to measurement error. The effect of tissue heterogeneity on measurement accuracy can be decreased by quantifying the probe sensing depth. To this end, this work (1) investigates the sensing depth of the 2.2 mm-diameter open-ended coaxial probe for skin mimicking material and (2) offers a simple experimental setup and protocol for sensing depth characterization of open-ended coaxial probes. The sensing depth characterized through simulation and experiments using two double-layered configurations composed to mimic the skin tissue heterogeneity. Three thresholds in percent increase of dielectric property measurements were chosen to determine the sensing depth. Based on the experiment results, it was concluded that the sensing depth was effected by the dielectric property contrast between the layers. That is, high contrast results in rapid change whereas low contrast results in a slower change in measured dielectric properties. It was also concluded that the sensing depth was independent of frequency between 0.5 to 6 GHz and was mostly determined by the material located immediately at the aperture of the probe.

Skin cancer is one of the most common types of cancer worldwide. Over the past few years, different approaches have been proposed to deal with automated skin cancer detection. Nonetheless, most of them are based only on dermoscopic images and do not take into account the patient clinical information, an important clue towards clinical diagnosis. In this work, we present an approach to fill this gap. First, we introduce a new dataset composed of clinical images, collected using smartphones, and clinical data related to the patient. Next, we propose a straightforward method that includes an aggregation mechanism in well-known deep learning models to combine features from images and clinical data. Last, we carry out experiments to compare the models' performance with and without using this mechanism. The results present an improvement of approximately 7% in balanced accuracy when the aggregation method is applied. Overall, the impact of clinical data on models' performance is significant and shows the importance of including these features on automated skin cancer detection., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

Optical interrogation of suspicious skin lesions is standard care in the management of skin cancer worldwide. Morphological and functional markers of malignancy are often combined to improve expert human diagnostic power. We propose the evaluation of the combination of two independent optical biomarkers of skin tumours concurrently. The morphological modality of reflectance confocal microscopy (RCM) is combined with the functional modality of laser Doppler flowmetry, which is capable of quantifying tissue perfusion. To realize the idea, we propose laser feedback interferometry as an implementation of RCM, which is able to detect the Doppler signal in addition to the confocal reflectance signal. Based on the proposed technique, we study numerical models of skin tissue incorporating two optical biomarkers of malignancy: (i) abnormal red blood cell velocities and concentrations and (ii) anomalous optical properties manifested through tissue confocal reflectance, using Monte Carlo simulation. We also conduct a laboratory experiment on a microfluidic channel containing a dynamic turbid medium, to validate the efficacy of the technique. We quantify the performance of the technique by examining a signal to background ratio (SBR) in both the numerical and experimental models, and it is shown that both simulated and experimental SBRs improve consistently using this technique. This work indicates the feasibility of an optical instrument, which may have a role in enhanced imaging of skin malignancies., Competing Interests: The authors declare no conflict of interest.

Background: It is often difficult to differentiate early melanomas from benign melanocytic nevi even by expert dermatologists, and the task is even more challenging for primary care physicians untrained in dermatology and dermoscopy. A computer system can provide an objective and quantitative evaluation of skin lesions, reducing subjectivity in the diagnosis., Objective: Our objective is to make a low-cost computer aided diagnostic tool applicable in primary care based on a consumer grade camera with attached dermatoscope, and compare its performance to that of experienced dermatologists., Methods and Materials: We propose several new image-derived features computed from automatically segmented dermoscopic pictures. These are related to the asymmetry, color, border, geometry, and texture of skin lesions. The diagnostic accuracy of the system is compared with that of three dermatologists., Results: With a data set of 206 skin lesions, 169 benign and 37 melanomas, the classifier was able to provide competitive sensitivity (86%) and specificity (52%) scores compared with the sensitivity (85%) and specificity (48%) of the most accurate dermatologist using only dermoscopic images., Conclusion: We show that simple statistical classifiers can be trained to provide a recommendation on whether a pigmented skin lesion requires biopsy to exclude skin cancer with a performance that is comparable to and exceeds that of experienced dermatologists., (Copyright © 2013 Elsevier B.V. All rights reserved.)