Type Of Skin Cancer Research Articles

In Vitro and In Vivo Studies of Melanoma Cell Migration by Antagonistic Mimetics of Adhesion Molecule L1CAM.

Melanoma, the deadliest type of skin cancer, has a high propensity to metastasize to other organs, including the brain, lymph nodes, lungs, and bones. While progress has been made in managing melanoma with targeted and immune therapies, many patients do not benefit from these current treatment modalities. Tumor cell migration is the initial step for invasion and metastasis. A better understanding of the molecular mechanisms underlying metastasis is crucial for developing therapeutic strategies for metastatic diseases, including melanoma. The cell adhesion molecule L1CAM (CD171, in short L1) is upregulated in many human cancers, enhancing tumor cell migration. Earlier studies showed that the small-molecule antagonistic mimetics of L1 suppress glioblastoma cell migration in vitro. This study aims to evaluate if L1 mimetic antagonists can inhibit melanoma cell migration in vitro and in vivo. We showed that two antagonistic mimetics of L1, anagrelide and 2-hydroxy-5-fluoropyrimidine (2H5F), reduced melanoma cell migration in vitro. In in vivo allograft studies, only 2H5F-treated female mice showed a decrease in tumor volume.

An overview of cutaneous squamous cell carcinoma imaging diagnosis methods.

Cutaneous squamous cell carcinoma, a type of non-melanoma skin cancer, is a form of keratinocyte carcinoma that stands as one of the most prevalent cancers, exhibiting a rising frequency. This review provides an overview of the latest literature on imaging methods for diagnosing squamous cell carcinoma (SCC) and actinic keratosis (AK). It discusses the diagnostic criteria, advantages, and disadvantages of various techniques such as dermatoscopy, skin ultrasound (US), in vivo and ex-vivo reflectance confocal microscopy (RCM), and line-field confocal optical coherence tomography (LC-OCT). These methods offer benefits including non-invasiveness, rapidity, comprehensive lesion imaging, and enhanced sensitivity, but face challenges like high costs and the need for specialized expertise. Despite obstacles, the use of these innovative techniques is expected to increase with ongoing technological advancements, improving diagnosis and treatment planning for keratinocyte carcinomas. Standardizing LC-OCT imaging algorithms for AK, Bowen's disease, and SCC remains an area for further research.

Systematic evaluation of Merkel cell carcinoma clinical practice guidelines using the AGREE II instrument

Merkel cell carcinoma (MCC) is a rare type of skin cancer that requires a multidisciplinary approach with a variety of specialists for management and treatment. Clinical practice guidelines (CPGs) have recently been established to standardize management algorithms. The objective of this study was to appraise such CPGs via the Appraisal of Guidelines for Research and Evaluation (AGREE II) instrument. Eight CPGs were identified via systematic literature search following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) criteria. Four appraisers trained in AGREE II protocols evaluated each CPG and deemed two CPGs as high quality, five as moderate quality, and one as low quality. Intraclass correlation coefficients (ICCs) were calculated to verify reviewer consistency as excellent, good, and moderate across four, one, and one domain, respectively. The majority of MCC CPGs are lacking in specifying stakeholder involvement, applicability, and rigor of development. The two high quality CPGs are from the Alberta Health Services (AHS) and the collaboration between the European Dermatology Forum, the European Association of Dermato-Oncology, and the European Organization of Research and Treatment of Cancer (EDF/EADO/EORTC). The EDF/EADO/EORTC CPG had the highest overall score with no significant deficiencies across any domain. An important limitation is that the AGREE II instrument is not designed to evaluate the validity of each CPG’s recommendations; conclusions therefore can only be drawn about each CPG’s developmental quality. Future MCC CPGs may benefit from garnering public perspectives, inviting external expert review, and considering available resources and implementation barriers during their developmental stages.

Personalized identification and characterization of genome-wide gene expression differences between patient-matched intracranial and extracranial melanoma metastasis pairs

Melanoma is the most serious type of skin cancer that frequently spreads to other organs of the human body. Especially melanoma metastases to the brain (intracranial metastases) are hard to treat and a major cause of death of melanoma patients. Little is known about molecular alterations and altered mechanisms that distinguish intra- from extracranial melanoma metastases. So far, almost all existing studies compared intracranial metastases from one set of patients to extracranial metastases of an another set of melanoma patients. This neglects the important facts that each melanoma is highly individual and that intra- and extracranial melanoma metastases from the same patient are more similar to each other than to melanoma metastases from other patients in the same organ. To overcome this, we compared the gene expression profiles of 16 intracranial metastases to their corresponding 21 patient-matched extracranial metastases in a personalized way using a three-state Hidden Markov Model (HMM) to identify altered genes for each individual metastasis pair. This enabled three major findings by considering the predicted gene expression alterations across all patients: (i) most frequently altered pathways include cytokine-receptor interaction, calcium signaling, ECM-receptor interaction, cAMP signaling, Jak-STAT and PI3K/Akt signaling, (ii) immune-relevant signaling pathway genes were downregulated in intracranial metastases, and (iii) intracranial metastases were associated with a brain-like phenotype gene expression program. Further, the integration of all differentially expressed genes across the patient-matched melanoma metastasis pairs led to a set of 103 genes that were consistently down- or up-regulated in at least 11 of the 16 of the patients. This set of genes contained many genes involved in the regulation of immune responses, cell growth, cellular signaling and transport processes. An analysis of these genes in the TCGA melanoma cohort showed that the expression behavior of 11 genes was significantly associated with survival. Moreover, a comparison of the 103 genes to three closely related melanoma metastasis studies revealed a core set of eight genes that were consistently down- or upregulated in intra- compared to extracranial metastases in at least two of the three related studies (down: CILP, DPT, FGF7, LAMP3, MEOX2, TMEM119; up: GLDN, PMP2) including FGF7 that was also significantly associated with survival. Our findings contribute to a better characterization of genes and pathways that distinguish intra- from extracranial melanoma metastasis and provide important hints for future experimental studies to identify potential targets for new therapeutic approaches.

Enhancing Skin Cancer Detection Through Deep Learning Techniques

Skin cancer is a common and possibly deadly disease that affects the skin's outer layers. Promoting knowledge of skin cancer, its risk factors, and the need for early detection can help combat the illness and reduce its impact on individuals and communities around the world. In this project, we describe a novel use of deep learning techniques to detect skin cancer in its early stages using dermatoscopic images. The primary goal of this study is to create an accurate and reliable model for predicting various types of skin cancer, including actinic keratoses and intraepithelial carcinoma (akiec), basal cell carcinoma (bcc), benign keratosis-like lesions (bkl), dermatofibroma (df), melanoma (mel), melanocytic nevi (nv), and pyogenic granulomas and hemorrhage (vasc). The project is written in Python, and the primary algorithm/model used is the Convolutional Neural Network (CNN) architecture. CNNs are ideal for image classification jobs because they can automatically learn relevant characteristics from input. Using CNNs, our model is trained on the HAM10000 (Human Against Machine with 10,000 training images) dataset, which contains 10015 high-resolution dermatoscopic images obtained from distinct populations and acquired via multiple modalities. The achieved findings illustrate the effectiveness of our proposed method. The model had an amazing training accuracy of 96.00% and a validation accuracy of 97.00%. These high accuracy rates demonstrate the promise of our deep learning-based skin cancer prediction system as a trustworthy tool for early diagnosis, allowing healthcare practitioners to make more informed decisions and improve patient outcomes. Our effort contributes to the field of dermatological research and machine learning, providing valuable insights into the use of deep learning algorithms for skin cancer prediction. Furthermore, the publicly available HAM10000 dataset, which includes a wide range of dermatoscopic pictures, can be a great resource for academic study and future advances in the field of medical image processing and classification. Key Words– benign, CNN, early detection, skin cancer, machine learning

Early detection of melanoma skin cancer: A hybrid approach using fuzzy C-means clustering and differential evolution-based convolutional neural network

Skin cancer is a prevalent type of disease that is challenging to predict, and early detection is crucial for successful treatment. In this study, we propose an improved strategy for early detection of three types of skin cancers using medical imaging. Our approach uses fuzzy C-means clustering for image segmentation, along with various filters and image features including Local Binary Pattern (LBP), RGB color-space, and GLCM methods. We also employ a Convolutional neural network (CNN) trained with differential evolution (DE) algorithm for classification. We evaluate the proposed technique using skin cancer image datasets HAM10000, and demonstrate its superior performance compared to traditional classifiers. Our approach achieves a detection accuracy of 91 %, which is significantly higher than other traditional methods in the same domain. To enhance the accuracy of skin cancer detection in medical imaging, the proposed technique can be automated using electronic devices like mobile phones.

Representation of skin carcinomas in public awareness - Awareness worldwide and in Germany.

In recent years, there has been an increase in skin cancer. The aim of this study was therefore to investigate the representation of skin cancer in public awareness worldwide and in Germany, and to determine whether Skin Cancer Awareness Month is represented in the search interests of the Internet-using population in the same way as Breast Cancer Awareness Month worldwide. In this study, Google Trends data were used to track levels of public awareness for different tumor entities and skin cancer types worldwide and for Germany. The results of this analysis clearly showed a high level of relative public search interest in breast cancer worldwide in the awareness month of October. Worldwide and in Germany, there was a certain increase in search interest and a certain seasonal effect around the May awareness month for skin cancer. For example, the analysis showed a search interest in May and during the summer months in Germany. It is likely that the population, for example in Germany, may benefit further from an even greater emphasis on the topic of skin cancer.

Classification of Melanoma Based on Clustering Channels RGB of Skin Scan Using Adaptive Light Weight Deep Learning System

Melanoma is considered to be a type of skin cancer that is characterisedby symptoms of poor prognostic responses. In this paper, we propose a deep learning method using DCNNs, modifying the output layer and enhancing the features of skin scan images collected from Kaggle to be distinguished into two groups: melanoma and non-melanoma cells. The proposed modified three types of DCNN (MobilNet-v2, ResNet-18 and Squeeze Net) models have been tested in two experiments. In the first, the obtained values of training accuracy are (93, 95 and 91) % and the testing accuracy is (90.09, 90.54 and 90.4) %, using original datasets only. In the second experiment, the obtained values of training accuracy are (99.7, 96.3 and 92) % and the testing accuracy is (94.41, 94.14 and 91.43) %, The experimental findings show that the model utilized produces enhanced photos with more accuracy than original images.

Literature Review: Skin Cancer Types Detection Using Deep Learning

Skin cancer ranks among the swiftly proliferating diseases globally, exacerbated by the limited resources. Timely recognition of skin cancer holds paramount importance for precise diagnosis and identification, facilitating a preventative approach overall. The incidence of melanoma, the most perilous type of cancer of the skin, is increasing. Identifying skin cancer in its initial phases poses a challenge for dermatologists. Over the past few years, both supervised and unsupervised learning assignments have extensively employed deep learning techniques. Among these, Convolutional Neural Networks (CNN) has outperformed its counterparts in tests related to object detection and classification. The algorithm used here relies on a bank of directional filters (difference of Gaussians) and explores color, directionality and topological properties of the network. Dull Razor algorithm has been used to remove artifacts such as hair as they cause difficulties in detecting pigments. Keywords— Deep learning, Convolutional Neural Networks (CNN), Color analysis, Data Augmentation, Artifacts removal, Pigment detection.

APPROACH AND TREATMENT OF NON-MELANOMA SKIN CANCER DURING PREGNANCY

Pregnancy presents unique challenges in diagnosing and treating non-melanoma skin cancer, the most common type of skin cancer. Hormonal and immunological changes during pregnancy can influence the development and progression of the disease, while ethical issues and concerns about fetal safety complicate the therapeutic approach. Therefore, it is crucial to understand how this condition is managed during pregnancy to ensure the best outcome for mother and baby. Objective: The objective of this systematic literature review is to analyze the approaches and treatments used for non-melanoma skin cancer during pregnancy, with a focus on maternal and fetal safety. Methodology: The review followed the PRISMA checklist guidelines. Articles published in the last 10 years were searched in the PubMed, Scielo and Web of Science databases. The descriptors used were "non-melanoma skin cancer", "pregnancy", "treatment", "approach" and "fetal safety". The inclusion criteria were studies that described cases of non-melanoma skin cancer during pregnancy, addressing treatment options and fetal safety considerations. The exclusion criteria were studies irrelevant to the topic, studies without access to the full text and studies not available in English, Portuguese or Spanish. Results: Analysis of the articles revealed several therapeutic approaches for non-melanoma skin cancer during pregnancy, including surgery, photodynamic therapy and cryotherapy. There was an emphasis on the individualized assessment of each case, taking into account the stage of the disease, the location of the tumor and the potential risks for the mother and fetus. Prevention was also highlighted, with an emphasis on sun protection and skin self-examination. However, some treatments have been delayed until after birth due to concerns about fetal safety. Conclusion: The management of non-melanoma skin cancer during pregnancy requires a multidisciplinary and individualized approach, considering the risks and benefits for the mother and fetus. Prevention, early diagnosis and adequate treatment are essential to guarantee the best result for both. More research is needed to further guide clinical practices in this area.

The Anticancer Activities of Natural Terpenoids That Inhibit Both Melanoma and Non-Melanoma Skin Cancers.

The prevalence of two major types of skin cancer, melanoma and non-melanoma skin cancer, has been increasing worldwide. Skin cancer incidence is estimated to rise continuously over the next 20 years due to ozone depletion and an increased life expectancy. Chemotherapeutic agents could affect healthy cells, and thus may be toxic to them and cause numerous side effects or drug resistance. Phytochemicals that are naturally occurring in fruits, plants, and herbs are known to possess various bioactive properties, including anticancer properties. Although the effects of phytochemicals are relatively milder than chemotherapeutic agents, the long-term intake of phytochemicals may be effective and safe in preventing tumor development in humans. Diverse phytochemicals have shown anti-tumorigenic activities for either melanoma or non-melanoma skin cancer. In this review, we focused on summarizing recent research findings of the natural and dietary terpenoids (eucalyptol, eugenol, geraniol, linalool, and ursolic acid) that have anticancer activities for both melanoma and non-melanoma skin cancers. These terpenoids may be helpful to protect skin collectively to prevent tumorigenesis of both melanoma and nonmelanoma skin cancers.

Impact of Sunscreen on Preventing Each Type of Facial Skin Cancer

The most frequent cancer in the US is skin cancer, around 5.4 million cases of Basal Cell Carcinoma (BCC) and Squamous Cell Carcinoma (SCC) are treated every year, while BCC is more common. Exposure to ultraviolet rays is the source of four varieties of skin cancer: BCC, SCC, Melanoma, and Merkel Cell Carcinoma (MCC). Since there are several experiments done on the influence of sunscreen on skin cancer, this paper aims to offer a comprehensive and symmetric conclusion to existing research. This paper summarizes the current studies finished for each type of skin cancer that might be caused by high levels of UV exposure. This study cites 4 experiments for melanoma, and three of them show a reduction in the rate of melanoma if sun blockers were used. This paper also uses three research for SCC and BCC to analyze the relationship between sun cream application and the probability of having SCC and BCC. None of them found that sunscreen will decrease the rate of SCC and one of them contends that sun lotion can prevent some of SCC. There is no current research about MCC, as the difficulty of holding this kind of experiment. This study will provide a reference for the medical field and the general public to analyze if sun lotion should be employed to prevent skin cancers.

Nanodelivery Approaches of Phytoactives for Skin Cancers: Current and Future Perspectives.

In recent years, there has been an increase in skin cancers due to external factors, especially environmental factors, and studies on treatment alternatives have gained importance. Nanomaterials are common, from sunscreen formulas to formulations designed to treat skin cancers at various stages. Using bioactives has multiple effects in treating skin cancers, which provides many advantages. In this regard, many phytochemicals gain importance with their antioxidant, anti-proliferative, anti-inflammatory, antiangiogenic, and analgesic effects. Their delivery with nanocarriers is on the agenda for phytochemicals to gain the targeted stability, effectiveness, and toxicity/safety properties. This review presents types of skin cancers, phytochemicals effective in skin cancers, and their nanocarrier-loaded studies from an up-to-date perspective.

Establishment and molecular characterization of HCB-541, a novel and aggressive human cutaneous squamous cell carcinoma cell line

Cutaneous squamous cell carcinoma (cSCC) is a common type of skin cancer that can result in significant morbidity, although it is usually well-managed and rarely metastasizes. However, the lack of commercially available cSCC cell lines hinders our understanding of this disease. This study aims to establish and characterize a new metastatic cSCC cell line derived from a Brazilian patient. A tumor biopsy was taken from a metastatic cSCC patient, immortalized, and named HCB-541 after several passages. The cytokeratin expression profile, karyotypic alterations, mutational analysis, mRNA and protein differential expression, tumorigenic capacity in xenograft models, and drug sensitivity were analyzed. The HCB-541 cell line showed a doubling time between 20 and 30 h and high tumorigenic capacity in the xenograft mouse model. The HCB-541 cell line showed hypodiploid and hypotetraploidy populations. We found pathogenic mutations in TP53 p.(Arg248Leu), HRAS (Gln61His) and TERT promoter (C228T) and high-level microsatellite instability (MSI-H) in both tumor and cell line. We observed 37 cancer-related genes differentially expressed when compared with HACAT control cells. The HCB-541 cells exhibited high phosphorylated levels of EGFR, AXL, Tie, FGFR, and ROR2, and high sensitivity to cisplatin, carboplatin, and EGFR inhibitors. Our study successfully established HCB-541, a new cSCC cell line that could be useful as a valuable biological model for understanding the biology and therapy of metastatic skin cancer.

Is beauty worth the risk? Self-confidence is the key motivating factor driving tanning bed use among undergraduate students at South Dakota universities.

Exposure to ultraviolet radiation is a leading risk factor for developing all types of skin cancer. In the United States, an estimated 7.8 million young adults engage in indoor tanning. Here, it is hypothesized that certain populations of students at undergraduate universities, namely sorority members, have a greater frequency of tanning bed usage than other groups of students and that regardless of sorority status, the most important motivating factor will be the intent to enhance one's appearance. Undergraduate students at 2 state-funded universities were recruited for participation in this institutional review board-exempt survey via distribution to e-mail addresses and social media accounts affiliated with student organizations/clubs. Among all respondents, the most common motivating factors for tanning bed use were the perception of improved self-appearance and boosted self-confidence. Female sorority members were more likely to use tanning beds and also more likely to report being motivated by enhanced appearance and self-confidence, than their female counterparts who were not sorority members. The sample size (n = 321) and population of this study allows data to only be generalizable to surrounding states with similar demographics. The findings of this study are subject to recall bias as the data is self-reported. Tanning bed use remains a popular practice among young people. Understanding motivations for tanning bed use among populations at increased risk of partaking in this behavior allows for educating these groups on the risks associated with ultraviolet radiation exposure. It is crucial that providers continue to promote skin health and take steps to dissuade detrimental practices and possible habit-forming behaviors at the individual and state levels.

Optimization of Skin Cancer Detection to Improve Accuracy with the Application of Efficient Convolutional Neural Network and EfficientNetB2 Models

Skin cancer, related to ultraviolet light exposure, can be caused by genetic mutations in skin cells. Risk factors include family history, fair skin, moles, weak immunity and solar keratosis. Diagnosis involves skin examination and biopsy. Previous studies using Convolutional Neural Network (CNN) successfully classified skin cancer with an accuracy of up to 99%. The process of developing an image classification model involves structured steps. The dataset is divided into training, validation, testing. Data augmentation is performed with ImageDataGenerator to enrich the dataset. CNN model (EfficientNetB2) was customized, trained for 50 epochs. Evaluation of testing data model, metrics such as loss, accuracy, precision, recall, and F1-score. Visualization of Classification Reports, Confusion Matrix. The entire process ensured in-depth analysis of the model's performance and focused attention on prediction. The study used Kaggle's secondary data "HAM10000 Preprocessed Data" with 11644 data and three attributes, showing variations before normalization. The CNN model peaked at the 8th epoch with 86% accuracy, but there is a risk of overfitting. Evaluation using Classification Report and Confusion Matrix provides details of model performance on each skin cancer class, supporting diagnosis and treatment.This article highlights the positive impact of using the EfficientNetB2 Model on skin cancer detection through an efficient Convolutional Neural Network (CNN). The model is optimal in size and structure, resulting in superior feature representation. Evaluations including Classification Report and Confusion Matrix demonstrate the model's superiority in classifying skin cancer types, particularly in the ''akiec'' and ''bcc'' classes, with high accuracy, significantly improving detection performance.

Application of Deep Learning with ResNet50 for Early Detection of Melanoma Skin Cancer

Cancer is a type of disease that can be fatal. Some of the cancers with the highest death rates in Indonesia include uterine cancer, breast cancer and skin cancer. The most malignant types of skin cancer are melanoma, which has a high mortality rate, especially if not detected in the early stages, and non-melanoma skin cancer (NMS Cs). Management of this disease depends on whether the type of skin cancer is malignant (malignant) or non-malignant (benign). Therefore, we need a system that can classify types of skin cancer with high accuracy. In this research, the author will use deep learning with the InceptionV3 and ResNet50 algorithms to carry out classification. The aim of this research is to classify types of skin cancer using the InceptionV3 and ResNet50 architecture. The skin cancer dataset used consists of two classes, namely Benign and Malignant, with a total of 3297 data, consisting of 660 data for testing and 2637 data for training. Research stages include data acquisition, preprocessing, classification, and analysis of results. Experimental results show that ResNet-50 produces the best performance with an accuracy level of 0.87. Innovations from this research include using a larger dataset, testing two deep learning architectures, modifying hyperparameters, and using a different layer architecture, which produces better accuracy than previous research. It is hoped that the results of this research can be applied to classify skin cancer more accurately.

Extraction of Shape and Texture Features of Dermoscopy Image for Skin Cancer Identification

Skin diseases are increasing and becoming a very serious problem. Skin cancer in general there are 2, namely melanoma and non-melanoma. Cases that are often encountered are in non-melanoma types. A critical factor in the treatment of skin cancer is early diagnosis. Doctors usually use the biopsy method to detect skin cancer. Computer-based technology provides convenient, cheaper, and faster diagnosis of skin cancer symptoms. This study aims to identify the type of skin cancer. The data used in the study were 6 types of skin cancer, namely Basal Cell Carcinoma, Dermatofibroma, Melanoma, Nevus image, Pigmented Benign Keratosis image, or Vascular Lesion, with a total of 60 dermoscopy images obtained from the Kaggle site. Dermoscopy image processing begins with a pre-processing process, which converts RGB images to LAB. After that, segmentation is carried out to separate objects from the background. The method of extracting shape and texture features is used to obtain the characteristics of dermoscopy images. As many as 2 types of shape features, namely eccentricity and metric, and 4 types of texture features, namely contrast, correlation, energy, and homogeneity. The result of this study is that it can identify the type of skin cancer based on image features that have been extracted using a program from the Matlab application. The technique of extracting shape and texture features is proven to work well in identifying the type of skin cancer. In the future it is expected to use more data, and add color features in identifying dermoscopy images.

Dermatological Malignancy Detection Using CNN

Abstract: Skin cancer is a prevalent and potentially life-threatening disease that continues to pose a significant public health concern. Starting stage detection Plays a crucial part in enhancing treatment efficacy. Furthermore, the advancement of deep learning has enabled the creation of automated frameworks for detecting maligant cancer. This proposed system utilizes a varied dataset encompassing various types of skin cancer, such as malignant melanomas and benign nevi, to educate a learning model. Utilizing CNN, pertinent features are automatically extracted from skin cancer images, facilitating precise classification. Assessment of the suggested system showcases encouraging outcomes concerning sensitivity, specificity, and overall precision. Comparative assessments against conventional techniques underscore the superior efficacy and effectiveness of the devised model in identifying potential skin cancer instances. Moreover, the system's interpretive capacity is heightened through the integration integration of attention mechanisms offering insights into the areas of focus within the skin cancer images

SNC_Net: Skin Cancer Detection by Integrating Handcrafted and Deep Learning-Based Features Using Dermoscopy Images

The medical sciences are facing a major problem with the auto-detection of disease due to the fast growth in population density. Intelligent systems assist medical professionals in early disease detection and also help to provide consistent treatment that reduces the mortality rate. Skin cancer is considered to be the deadliest and most severe kind of cancer. Medical professionals utilize dermoscopy images to make a manual diagnosis of skin cancer. This method is labor-intensive and time-consuming and demands a considerable level of expertise. Automated detection methods are necessary for the early detection of skin cancer. The occurrence of hair and air bubbles in dermoscopic images affects the diagnosis of skin cancer. This research aims to classify eight different types of skin cancer, namely actinic keratosis (AKs), dermatofibroma (DFa), melanoma (MELa), basal cell carcinoma (BCCa), squamous cell carcinoma (SCCa), melanocytic nevus (MNi), vascular lesion (VASn), and benign keratosis (BKs). In this study, we propose SNC_Net, which integrates features derived from dermoscopic images through deep learning (DL) models and handcrafted (HC) feature extraction methods with the aim of improving the performance of the classifier. A convolutional neural network (CNN) is employed for classification. Dermoscopy images from the publicly accessible ISIC 2019 dataset for skin cancer detection is utilized to train and validate the model. The performance of the proposed model is compared with four baseline models, namely EfficientNetB0 (B1), MobileNetV2 (B2), DenseNet-121 (B3), and ResNet-101 (B4), and six state-of-the-art (SOTA) classifiers. With an accuracy of 97.81%, a precision of 98.31%, a recall of 97.89%, and an F1 score of 98.10%, the proposed model outperformed the SOTA classifiers as well as the four baseline models. Moreover, an Ablation study is also performed on the proposed method to validate its performance. The proposed method therefore assists dermatologists and other medical professionals in early skin cancer detection.