Dermoscopic characteristics of Merkel cell carcinoma.

BACKGROUND: Merkel cell carcinoma (MCC) is a rare, aggressive, cutaneous tumour with high mortality and frequently delayed diagnosis. Clinically, it often manifests as a rapidly growing erythematous to purple nodule usually located on the lower extremities or face and scalp of elderly patients. There is limited available data on the dermoscopic findings of MCC, and there are no specific features that can be used to definitively diagnose MCC. AIM OF THE STUDY: Here, we aimed to summarize existing published literature on dermatoscopic and reflectance confocal microscopy (RCM) features of MCC. MATERIALS AND METHODS: To find relevant studies, we searched the PubMed and Scopus databases from inception to April 12, 2023. Our goal was to identify all pertinent research that had been written in English. The following search strategy was employed: (" dermoscopy" OR " dermatoscopy" OR " videodermoscopy" OR " videodermatoscopy" OR " reflectance confocal microscopy") AND " Merkel cell carcinoma". Two dermatologists, DK and GE, evaluated the titles and abstracts separately for eligibility. For inclusion, only works written in English were taken into account. RESULTS: In total 16 articles were retrieved (68 cases). The main dermoscopic findings of MCC are a polymorphous vascular pattern including linear irregular, arborizing, glomerular, and dotted vessels on a milky red background, with shiny or non-shiny white areas. Pigmentation was lacking in all cases. The RCM images showed a thin and disarranged epidermis, and small hypo-reflective cells that resembled lymphocytes arranged in solid aggregates outlined by fibrous tissue in the dermis. Additionally, there were larger polymorphic hyper-reflective cells that likely represented highly proliferative cells. CONCLUSION: Dermoscopic findings of MCC may play a valuable role in evaluating MCC, aiding in the early detection and differentiation from other skin lesions. Further prospective case-control studies are needed to validate these results.

Reflectance confocal microscopy versus dermoscopy for the diagnosis of cutaneous melanoma: a head-to-head comparative meta-analysis.

This meta-analysis aimed to evaluate the comparative diagnostic performance of reflectance confocal microscopy (RCM) and dermoscopy in detecting cutaneous melanoma patients. An extensive search was conducted in the PubMed and Embase databases to identify available publications up to December 2023. Studies were included if they evaluated the diagnostic performance of RCM and dermoscopy in patients with cutaneous melanoma. The quality of the included studies was assessed using the Quality Assessment of Diagnostic Performance Studies (QUADAS-2) tool. A total of 14 articles involving 2013 patients were included in the meta-analysis. The overall sensitivity of RCM was 0.94 [95% confidence interval (CI), 0.87-0.98], while the overall sensitivity of dermoscopy was 0.84 (95% CI, 0.71-0.95). These results suggested that RCM has a similar level of sensitivity compared with dermoscopy ( P  = 0.15). In contrast, the overall specificity of RCM was 0.76 (95% CI, 0.67-0.85), while the overall specificity of dermoscopy was 0.47 (95% CI, 0.31-0.63). The results indicated that RCM appears to have a higher specificity in comparison to dermoscopy ( P  < 0.01). Our meta-analysis indicates that RCM demonstrates superior specificity and similar sensitivity to dermoscopy in detecting cutaneous melanoma patients. The high heterogeneity, however, may impact the evidence of the current study, further larger sample prospective research is required to confirm these findings.

Understanding the Dermoscopic Patterns of Basal Cell Carcinoma Using Line-Field Confocal Tomography.

Basal cell carcinoma (BCC) is the most frequent malignancy in the general population. To date, dermoscopy is considered a key tool for the diagnosis of BCC; nevertheless, line-field confocal optical coherence tomography (LC-OCT), a new non-invasive optical technique, has become increasingly important in clinical practice, allowing for in vivo imaging at cellular resolution. The present study aimed to investigate the possible correlation between the dermoscopic features of BCC and their LC-OCT counterparts. In total, 100 histopathologically confirmed BCC cases were collected at the Dermatologic Clinic of the University of Siena, Italy. Predefined dermoscopic and LC-OCT criteria were retrospectively evaluated, and their frequencies were calculated. The mean (SD) age of our cohort was 65.46 (13.36) years. Overall, BCC lesions were mainly located on the head (49%), and they were predominantly dermoscopically pigmented (59%). Interestingly, all dermoscopic features considered had a statistically significant agreement with the LC-OCT criteria (all p < 0.05). In conclusion, our results showed that dermoscopic patterns may be associated with LC-OCT findings, potentially increasing accuracy in BCC diagnosis. However, further studies are needed in this field.

A novel deep learning framework for accurate melanoma diagnosis integrating imaging and genomic data for improved patient outcomes.

BACKGROUND: Melanoma is one of the most malignant forms of skin cancer, with a high mortality rate in the advanced stages. Therefore, early and accurate detection of melanoma plays an important role in improving patients' prognosis. Biopsy is the traditional method for melanoma diagnosis, but this method lacks reliability. Therefore, it is important to apply new methods to diagnose melanoma effectively. AIM: This study presents a new approach to classify melanoma using deep neural networks (DNNs) with combined multiple modal imaging and genomic data, which could potentially provide more reliable diagnosis than current medical methods for melanoma. METHOD: We built a dataset of dermoscopic images, histopathological slides and genomic profiles. We developed a custom framework composed of two widely established types of neural networks for analysing image data Convolutional Neural Networks (CNNs) and networks that can learn graph structure for analysing genomic data-Graph Neural Networks. We trained and evaluated the proposed framework on this dataset. RESULTS: The developed multi-modal DNN achieved higher accuracy than traditional medical approaches. The mean accuracy of the proposed model was 92.5% with an area under the receiver operating characteristic curve of 0.96, suggesting that the multi-modal DNN approach can detect critical morphologic and molecular features of melanoma beyond the limitations of traditional AI and traditional machine learning approaches. The combination of cutting-edge AI may allow access to a broader range of diagnostic data, which can allow dermatologists to make more accurate decisions and refine treatment strategies. However, the application of the framework will have to be validated at a larger scale and more clinical trials need to be conducted to establish whether this novel diagnostic approach will be more effective and feasible.

Mohs micrographic surgery challenges and new technologies to optimize care of cutaneous malignancies of the ear.

Cutaneous malignancies affecting the ear, exacerbated by extensive ultraviolet (UV) exposure, pose intricate challenges owing to the organ's complex anatomy. This article investigates how the anatomy contributes to late-stage diagnoses and ensuing complexities in surgical interventions. Mohs Micrographic Surgery (MMS), acknowledged as the gold standard for treating most cutaneous malignancies of the ear, ensures superior margin control and cure rates. However, the ear's intricacy necessitates careful consideration of tissue availability and aesthetic outcomes. The manuscript explores new technologies like Reflectance Confocal Microscopy (RCM), Optical Coherence Tomography (OCT), High-Frequency, High-Resolution Ultrasound (HFHRUS), and Raman spectroscopy (RS). These technologies hold the promise of enhancing diagnostic accuracy and providing real-time visualization of excised tissue, thereby improving tumor margin assessments. Dermoscopy continues to be a valuable non-invasive tool for identifying malignant lesions. Staining methods in Mohs surgery are discussed, emphasizing hematoxylin and eosin (H&E) as the gold standard for evaluating tumor margins. Toluidine blue is explored for potential applications in assessing basal cell carcinomas (BCC), and immunohistochemical staining is considered for detecting proteins associated with specific malignancies. As MMS and imaging technologies advance, a thorough evaluation of their practicality, cost-effectiveness, and benefits becomes essential for enhancing surgical outcomes and patient care. The potential synergy of artificial intelligence with these innovations holds promise in revolutionizing tumor detection and improving the efficacy of cutaneous malignancy treatments.

Multicentric reticulohistiocytosis revealing a ductal breast cancer in situ: A case report with dermoscopic and histopathological findings. / Reticulohistiocitosis multicéntrica reveladora de cáncer de mama ductal in situ: reporte de caso con aspectos dermatoscópicos e histopatológicos.

Multicentric reticulohistiocytosis is a rare non-Langerhans cell histiocytosis of unknown etiology. It is classified as multicentric because of multisystem involvement. The disease predominantly affects the skin and joints, but visceral involvement is possible. Multiple erythematous-brownish, pruritic nodules and papules on the face, hands, neck, and trunk are characteristic. It is associated with autoimmune diseases, or malignant neoplasms are seen in 20% to 30% of patients with multicentric reticulohistiocytosis. The diagnosis is based on histopathology of affected tissues. As it is an underreported disease, there is no standardized treatment. A case of multicentric reticulohistiocytosis is reported as a paraneoplastic manifestation of ductal breast cancer, being successfully treated with no recurrence after two years of follow-up. Few cases of multicentric reticulohistiocytosis associated with breast cancer have been reported in the literature.

La reticulohistiocitosis multicéntrica es una enfermedad inflamatoria, una histiocitosis de células no Langerhans, poco frecuente y de etiología desconocida. Se clasifica como multicéntrica al presentar compromiso multisistémico. La enfermedad afecta predominantemente a la piel y las articulaciones, pero es posible la afectación visceral. Las manifestaciones cutáneas se caracterizan por múltiples nódulos y pápulas de color eritemato-marronáceas, pruriginosas en la cara, manos, cuello y tronco. Se asocia a enfermedades autoinmunes y neoplasias malignas, observándose entre el 20 y el 30% de los pacientes con reticulohistiocitosis multicéntrica. Su diagnóstico se realiza sobre la base de la histopatología de tejidos afectados. Al ser una enfermedad poco reportada, no existe tratamiento estandarizado. Se reporta un caso de reticulohistiocitosis multicéntrica como manifestación paraneoplásica a un cáncer ductal de mama, siendo tratadas con éxito, sin recidivas luego de dos años de seguimiento. Pocos casos se han reportado en la literatura de reticulohistiocitosis multicéntrica asociado a cáncer mamario.

Ultraviolet-induced red fluorescence in androgenetic alopecia-indicating alterations in microbial composition.

BACKGROUND: Ultraviolet (UV)-induced fluorescence technology is widely used in dermatology to identify microbial infections. Our clinical observations under an ultraviolet-induced fluorescent dermatoscope (UVFD) showed red fluorescence on the scalps of androgenetic alopecia (AGA) patients. In this study, based on the hypothesis that microbes are induced to emit red fluorescence under UV light, we aimed to explore the microbial disparities between the AGA fluorescent area (AF group) and AGA non-fluorescent area (ANF group). METHODS: Scalp swab samples were collected from 36 AGA patients, including both fluorescent and non-fluorescent areas. The bacterial communities on the scalp were analyzed by 16S rRNA gene sequencing and bioinformatics analysis, as well as through microbial culture methods. RESULTS: Significant variations were observed in microbial evenness, abundance composition, and functional predictions between fluorescent and non-fluorescent areas. Sequencing results highlighted significant differences in Cutibacterium abundance between these areas (34.06% and 21.36%, respectively; p < 0.05). Furthermore, cultured red fluorescent colonies primarily consisted of Cutibacterium spp., Cutibacterium acnes, Staphylococcus epidermidis, and Micrococcus spp. CONCLUSIONS: This is the first study to investigate scalp red fluorescence, highlighting microbial composition variability across different scalp regions. These findings may provide novel insights into the microbiological mechanisms of AGA.

Diagnostic accuracy of high-frequency ultrasound for cutaneous neoplasms: a narrative review of the literature.

High-frequency ultrasound has been used to visualize depth and vascularization of cutaneous neoplasms, but little has been synthesized as a review for a robust level of evidence about the diagnostic accuracy of high-frequency ultrasound in dermatology. A narrative review of the PubMed database was performed to establish the correlation between ultrasound findings and histopathologic/dermoscopic findings for cutaneous neoplasms. Articles were divided into the following four categories: melanocytic, keratinocytic/epidermal, appendageal, and soft tissue/neural neoplasms. Review of the literature revealed that ultrasound findings and histopathology findings were strongly correlated regarding the depth of a cutaneous neoplasm. Morphological characteristics were correlated primarily in soft tissue/neural neoplasms. Overall, there is a paucity of literature on the correlation between high-frequency ultrasound and histopathology of cutaneous neoplasms. Further studies are needed to investigate this correlation in various dermatologic conditions.

Automated Prediction of Malignant Melanoma using Two-Stage Convolutional Neural Network.

PURPOSE: A skin lesion refers to an area of the skin that exhibits anomalous growth or distinctive visual characteristics compared to the surrounding skin. Benign skin lesions are noncancerous and generally pose no threat. These irregular skin growths can vary in appearance. On the other hand, malignant skin lesions correspond to skin cancer, which happens to be the most prevalent form of cancer in the United States. Skin cancer involves the unusual proliferation of skin cells anywhere on the body. The conventional method for detecting skin cancer is relatively more painful. METHODS: This work involves the automated prediction of skin cancer and its types using two stage Convolutional Neural Network (CNN). The first stage of CNN extracts low level features and second stage extracts high level features. Feature selection is done using these two CNN and ABCD (Asymmetry, Border irregularity, Colour variation, and Diameter) technique. The features extracted from the two CNNs are fused with ABCD features and fed into classifiers for the final prediction. The classifiers employed in this work include ensemble learning methods such as gradient boosting and XG boost, as well as machine learning classifiers like decision trees and logistic regression. This methodology is evaluated using the International Skin Imaging Collaboration (ISIC) 2018 and 2019 dataset. RESULTS: As a result, the first stage CNN which is used for creation of new dataset achieved an accuracy of 97.92%. Second stage CNN which is used for feature selection achieved an accuracy of 98.86%. Classification results are obtained for both with and without fusion of features. CONCLUSION: Therefore, two stage prediction model achieved better results with feature fusion.

Comprehensive analysis of clinical images contributions for melanoma classification using convolutional neural networks.

BACKGROUND: Timely diagnosis plays a critical role in determining melanoma prognosis, prompting the development of deep learning models to aid clinicians. Questions persist regarding the efficacy of clinical images alone or in conjunction with dermoscopy images for model training. This study aims to compare the classification performance for melanoma of three types of CNN models: those trained on clinical images, dermoscopy images, and a combination of paired clinical and dermoscopy images from the same lesion. MATERIALS AND METHODS: We divided 914 image pairs into training, validation, and test sets. Models were built using pre-trained Inception-ResNetV2 convolutional layers for feature extraction, followed by binary classification. Training comprised 20 models per CNN type using sets of random hyperparameters. Best models were chosen based on validation AUC-ROC. RESULTS: Significant AUC-ROC differences were found between clinical versus dermoscopy models (0.661 vs. 0.869, p < 0.001) and clinical versus clinical + dermoscopy models (0.661 vs. 0.822, p = 0.001). Significant sensitivity differences were found between clinical and dermoscopy models (0.513 vs. 0.799, p = 0.01), dermoscopy versus clinical + dermoscopy models (0.799 vs. 1.000, p = 0.02), and clinical versus clinical + dermoscopy models (0.513 vs. 1.000, p < 0.001). Significant specificity differences were found between dermoscopy versus clinical + dermoscopy models (0.800 vs. 0.288, p < 0.001) and clinical versus clinical + dermoscopy models (0.650 vs. 0.288, p < 0.001). CONCLUSION: CNN models trained on dermoscopy images outperformed those relying solely on clinical images under our study conditions. The potential advantages of incorporating paired clinical and dermoscopy images for CNN-based melanoma classification appear less clear based on our findings.

A novel approach for melanoma detection utilizing GAN synthesis and vision transformer.

BACKGROUND AND OBJECTIVE: Melanoma, a malignant form of skin cancer, is a critical health concern worldwide. Early and accurate detection plays a pivotal role in improving patient's conditions. Current diagnosis of skin cancer largely relies on visual inspections such as dermoscopy examinations, clinical screening and histopathological examinations. However, these approaches are characterized by low efficiency, high costs, and a lack of guaranteed accuracy. Consequently, deep learning based techniques have emerged in the field of melanoma detection, successfully aiding in improving the accuracy of diagnosis. However, the high similarity between benign and malignant melanomas, combined with the class imbalance issue in skin lesion datasets, present a significant challenge in further improving the diagnosis accuracy. We propose a two-stage framework for melanoma detection to address these issues. METHODS: In the first stage, we use Style Generative Adversarial Networks with Adaptive discriminator augmentation synthesis to generate realistic and diverse melanoma images, which are then combined with the original dataset to create an augmented dataset. In the second stage, we utilize a vision Transformer of BatchFormer to extract features and detect melanoma or non-melanoma skin lesions on the augmented dataset obtained in the previous step, specifically, we employed a dual-branch training strategy in this process. RESULTS: Our experimental results on the ISIC2020 dataset demonstrate the effectiveness of the proposed approach, showing a significant improvement in melanoma detection. The method achieved an accuracy of 98.43%, an AUC value of 98.63%, and an F1 value of 99.01%, surpassing some existing methods. CONCLUSION: The method is feasible, efficient, and achieves early melanoma screening. It significantly enhances detection accuracy and can assist physicians in diagnosis to a great extent.

Multi-scale feature fusion and class weight loss for skin lesion classification.

Skin cancer is one of the common types of cancer. It spreads quickly and is not easy to detect in the early stages, posing a major threat to human health. In recent years, deep learning methods have attracted widespread attention for skin cancer detection in dermoscopic images. However, training a practical classifier becomes highly challenging due to inter-class similarity and intra-class variation in skin lesion images. To address these problems, we propose a multi-scale fusion structure that combines shallow and deep features for more accurate classification. Simultaneously, we implement three approaches to the problem of class imbalance: class weighting, label smoothing, and resampling. In addition, the HAM10000_RE dataset strips out hair features to demonstrate the role of hair features in the classification process. We demonstrate that the region of interest is the most critical classification feature for the HAM10000_SE dataset, which segments lesion regions. We evaluated the effectiveness of our model using the HAM10000 and ISIC2019 dataset. The results showed that this method performed well in dermoscopic classification tasks, with ACC and AUC of 94.0% and 99.3%, on the HAM10000 dataset and ACC of 89.8% for the ISIC2019 dataset. The overall performance of our model is excellent in comparison to state-of-the-art models.

Therapeutic and fluorescence evaluation of 20% 5-aminolevulinic acid-mediated photodynamic therapy in actinic keratosis.

BACKGROUND: Actinic keratosis (AK) is a precancerous lesion that occurs in areas that are chronically exposed to sunlight and has the potential to develop into invasive cutaneous squamous cell carcinoma (cSCC). We investigated the efficacy of 20 % 5-aminolevulinic acid-photodynamic therapy (ALA-PDT) with LED red light for the treatment of AK in Chinese patients by examining changes in dermoscopic features, histopathology and fluorescence after treatment. METHODS: Twenty-eight patients with fourty-six AK lesions from March 2022 to September 2023 were treated with 20 % ALA, and 3 h later, they were irradiated with LED red light (80-100 mW/cm2) for 20 min. A session of 20 % ALA-PDT was performed once a week for three consecutive weeks, and the dermoscopic, histopathological, fluorescent and photoaging outcomes were measured one week after the treatment. RESULTS: One week after ALA-PDT, complete remission (CR) was reached in 53.6 % of patients. The CR of Grade I AK lesions was 100 %, that of Grade II lesions was 71.4 %, and that of Grade III lesions was 38.1 %. There was a significant improvement in the dermoscopic features, epidermal thickness and fluorescence of the AK lesions. The presence of red fluorescence decreased, and there was an association between CR and post-PDT fluorescence intensity. ALA-PDT also exhibited efficacy in treating photoaging, including fine lines, sallowness, mottled pigmentation, erythema, and telangiectasias, and improved the global score for photoaging. There were no serious adverse effects during or after ALA-PDT, and 82.1 % of the patients were satisfied with the treatment. CONCLUSION: AK lesions can be safely and effectively treated with 20 % ALA-PDT with LED red light, which also alleviates photoaging in Chinese patients, including those with multiple AKs. This study highlights the possibility that fluorescence could be used to diagnose AK with peripheral field cancerization and evaluate the efficacy of ALA-PDT.

Dermoscopy to differentiate clinically similar inflammatory and neoplastic skin lesions.

INTRODUCTION: Over the few last decades, dermoscopy has become an invaluable and popular imaging technique that complements the diagnostic armamentarium of dermatologists, being employed for both tumors and inflammatory diseases. Whereas distinction between neoplastic and inflammatory lesions is often straightforward based on clinical data, there are some scenarios that may be troublesome, e.g., solitary inflammatory lesions or tumors superimposed to a widespread inflammatory condition that may share macroscopic morphological findings. EVIDENCE ACQUISITION: We reviewed the literature to identify dermoscopic clues to support the differential diagnosis of clinically similar inflammatory and neoplastic skin lesions, also providing the histological background of such dermoscopic points of differentiation. EVIDENCE SYNTHESIS: Dermoscopic differentiating features were identified for 12 relatively common challenging scenarios, including Bowen's disease and basal cell carcinoma vs. psoriasis and dermatitis, erythroplasia of Queyrat vs. inflammatory balanitis, mammary and extramammary Paget's disease vs. inflammatory mimickers, actinic keratoses vs. discoid lupus erythematosus, squamous cell carcinoma vs. hypertrophic lichen planus and lichen simplex chronicus, actinic cheilitis vs. inflammatory cheilitis, keratoacanthomas vs. prurigo nodularis, nodular lymphomas vs. pseudolymphomas and inflammatory mimickers, mycosis fungoides vs. parapsoriasis and inflammatory mimickers, angiosarcoma vs granuloma faciale, and Kaposi sarcoma vs pseudo-Kaposi. CONCLUSIONS: Dermoscopy may be of aid in differentiating clinically similar inflammatory and neoplastic skin lesions.

Enhancing assisted diagnostic accuracy in scalp psoriasis: A Multi-Network Fusion Object Detection Framework for dermoscopic pattern diagnosis.

BACKGROUND: Dermoscopy is a common method of scalp psoriasis diagnosis, and several artificial intelligence techniques have been used to assist dermoscopy in the diagnosis of nail fungus disease, the most commonly used being the convolutional neural network algorithm; however, convolutional neural networks are only the most basic algorithm, and the use of object detection algorithms to assist dermoscopy in the diagnosis of scalp psoriasis has not been reported. OBJECTIVES: Establishment of a dermoscopic modality diagnostic framework for scalp psoriasis based on object detection technology and image enhancement to improve diagnostic efficiency and accuracy. METHODS: We analyzed the dermoscopic patterns of scalp psoriasis diagnosed at 72nd Group army hospital of PLA from January 1, 2020 to December 31, 2021, and selected scalp seborrheic dermatitis as a control group. Based on dermoscopic images and major dermoscopic patterns of scalp psoriasis and scalp seborrheic dermatitis, we investigated a multi-network fusion object detection framework based on the object detection technique Faster R-CNN and the image enhancement technique contrast limited adaptive histogram equalization (CLAHE), for assisting in the diagnosis of scalp psoriasis and scalp seborrheic dermatitis, as well as to differentiate the major dermoscopic patterns of the two diseases. The diagnostic performance of the multi-network fusion object detection framework was compared with that between dermatologists. RESULTS: A total of 1876 dermoscopic images were collected, including 1218 for scalp psoriasis versus 658 for scalp seborrheic dermatitis. Based on these images, training and testing are performed using a multi-network fusion object detection framework. The results showed that the test accuracy, specificity, sensitivity, and Youden index for the diagnosis of scalp psoriasis was: 91.0%, 89.5%, 91.0%, and 0.805, and for the main dermoscopic patterns of scalp psoriasis and scalp seborrheic dermatitis, the diagnostic results were: 89.9%, 97.7%, 89.9%, and 0.876. Comparing the diagnostic results with those of five dermatologists, the fusion framework performs better than the dermatologists' diagnoses. CONCLUSIONS: Studies have shown some differences in dermoscopic patterns between scalp psoriasis and scalp seborrheic dermatitis. The proposed multi-network fusion object detection framework has higher diagnostic performance for scalp psoriasis than for dermatologists.

A novel SpaSA based hyper-parameter optimized FCEDN with adaptive CNN classification for skin cancer detection.

Skin cancer is the most prevalent kind of cancer in people. It is estimated that more than 1 million people get skin cancer every year in the world. The effectiveness of the disease's therapy is significantly impacted by early identification of this illness. Preprocessing is the initial detecting stage in enhancing the quality of skin images by removing undesired background noise and objects. This study aims is to compile preprocessing techniques for skin cancer imaging that are currently accessible. Researchers looking into automated skin cancer diagnosis might use this article as an excellent place to start. The fully convolutional encoder-decoder network and Sparrow search algorithm (FCEDN-SpaSA) are proposed in this study for the segmentation of dermoscopic images. The individual wolf method and the ensemble ghosting technique are integrated to generate a neighbour-based search strategy in SpaSA for stressing the correct balance between navigation and exploitation. The classification procedure is accomplished by using an adaptive CNN technique to discriminate between normal skin and malignant skin lesions suggestive of disease. Our method provides classification accuracies comparable to commonly used incremental learning techniques while using less energy, storage space, memory access, and training time (only network updates with new training samples, no network sharing). In a simulation, the segmentation performance of the proposed technique on the ISBI 2017, ISIC 2018, and PH2 datasets reached accuracies of 95.28%, 95.89%, 92.70%, and 98.78%, respectively, on the same dataset and assessed the classification performance. It is accurate 91.67% of the time. The efficiency of the suggested strategy is demonstrated through comparisons with cutting-edge methodologies.