Early Detection Of Skin Cancer Research Articles

Convolutional Neural Networks Framework for the Early Classification and Detection of Melanoma Skin Cancer

Convolutional Neural Networks Framework for the Early Classification and Detection of Melanoma Skin Cancer

Integrating microwave reflectometry and deep learning imaging for in-vivo skin cancer diagnostics

Skin cancer is a major global health concern, with rising incidence rates. However, current diagnostic methods often lack objectivity, speed, and non-invasiveness. To overcome this limitation, this work proposes a novel approach combining microwave reflectometry (MR) with artificial intelligence (AI) techniques for the diagnosis of in-vivo skin cancer. In particular, MR exploits the dielectric properties of biological tissues, revealing chemical-physical differences in normal skin and benign/malignant lesions. To better improve the diagnostic performance, MR analysis is integrated with AI algorithms, particularly those based on deep learning (DL) and convolutional neural networks (CNNs), which analyze dermoscopic skin images and identify asymmetry, irregular borders, abnormal colorations, and other skin cancer indicators. Combining MR with AI image analysis provides a comprehensive diagnostic approach, as MR informs on tissue dielectric composition, while AI analyzes lesion-specific details, offering more accurate and timely assessments. This combined approach promises early skin cancer detection and could significantly impact clinical practice.

Automatic Melanoma Skin Cancer Detection and Segmentation using Snakecut Algorithm

Early detection of melanoma skin cancer is crucial for effective treatment, and computer-aided diagnostic technologies offer promising advancements for dermatologists to make faster, more precise diagnoses of skin lesions. Segmenting skin lesions is a crucial first step towards automated Computer-Aided Diagnosis for skin cancer. This paper aims to use SnakeCut, a foreground extraction approach, to automatically segment skin lesions in HSV color space with little human interaction. Active contour (otherwise called Snake) and Improved GrabCut are the two popular methods. By decreasing the energy function of the related contour, the active contour acts as a deformable segmentation contour. Improved GrabCut uses updated iterated graph cuts to store color attributes used as segmentation signals in order to achieve foreground segmentation from close-by pixel similarities in its foreground segmentation algorithm. The proposed integrated solution, which is predicated on a probabilistic framework, is termed “SnakeCut.” We removed the outer black border using preprocessing. Later feature extraction is done using HOG and HSV and classifies the benign or melanoma state using Naïve Bayes, Decision tree, and K-nearest neighbor classifiers. The efficiency of the segmentation strategy was measured using the Jaccard Index. We compared the classification results of our method with existing state-of-the-art approaches. The study demonstrates the efficacy of Automatic SnakeCut in accurately segmenting skin lesions, thereby enhancing the performance of subsequent classification tasks. The average F-score was 0.75 on the 2017 ISIC challenge training dataset of 100 images. Compared to other methods, this study’s findings reveal that the suggested method is highly accurate.

Early automated detection system for skin cancer diagnosis using artificial intelligent techniques

Recently, skin cancer is one of the spread and dangerous cancers around the world. Early detection of skin cancer can reduce mortality. Traditional methods for skin cancer detection are painful, time-consuming, expensive, and may cause the disease to spread out. Dermoscopy is used for noninvasive diagnosis of skin cancer. Artificial Intelligence (AI) plays a vital role in diseases’ diagnosis especially in biomedical engineering field. The automated detection systems based on AI reduce the complications in the traditional methods and can improve skin cancer’s diagnosis rate. In this paper, automated early detection system for skin cancer dermoscopic images using artificial intelligent is presented. Adaptive snake (AS) and region growing (RG) algorithms are used for automated segmentation and compared with each other. The results show that AS is accurate and efficient (accuracy = 96%) more than RG algorithm (accuracy = 90%). Artificial Neural networks (ANN) and support vector machine (SVM) algorithms are used for automated classification compared with each other. The proposed system with ANN algorithm shows high accuracy (94%), precision (96%), specificity (95.83%), sensitivity (recall) (92.30%), and F1-score (0.94). The proposed system is easy to use, time consuming, enables patients to make early detection for skin cancer and has high efficiency.

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.

Early detection of skin cancer using AI: Deciphering dermatology images for melanoma detection

This Review explores the transformative impact of artificial intelligence (AI) on the early detection of skin cancer, with a specific focus on melanoma, a potentially lethal form of the disease. Beginning with an overview of traditional diagnostic methods and their limitations, this paper delves into the evolution of AI within dermatology, emphasizing its application in image analysis and pattern recognition. A comprehensive examination of AI algorithms for melanoma detection, including machine learning and deep learning models, is provided. This Review critically assesses the performance metrics, training datasets, and comparative analyses with traditional methods. Addressing challenges such as data quality, interpretability, and ethical considerations, this paper outlines future directions, emphasizing ongoing research, algorithm improvements, and integration with clinical practices. Case studies and success stories highlight the real-world impact of AI in dermatology. This Review concludes by summarizing key findings and underlining the pivotal role of AI in revolutionizing early melanoma detection, with implications for personalized medicine and enhanced patient outcomes.

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.

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.

Optimizing time prediction and error classification in early melanoma detection using a hybrid RCNN-LSTM model.

Skin cancer is a terrifying disorder that affects all individuals. Due to the significant increase in the rate of melanoma skin cancer, early detection of skin cancer is now more critical than ever before. Malignant melanoma is one of the most serious forms of skin cancer, and it is caused by abnormal melanocyte cell growth. In recent years, skin cancer predictive categorization has become more accurate and predictive due to multiple deep learning algorithms. Malignant melanoma is diagnosed using the Recurrent Convolution Neural Network-Long Short-Term Memory (RCNN-LSTM), which is one of the deep learning classification approaches. Using the International Skin Image Collection and the RCNN-LSTM, the data are categorized and analyzed to gain a better understanding of skin cancer. The method begins with data preprocessing, which prepares the dataset for classification. Additionally, the RCNN is employed to extract the features that are vital to the prediction process. The LSTM is accountable for the final step, classification. There are further factors to examine, such as the precision of 94.60%, the sensitivity of 95.67%, and the F1-score of 95.13%. Other benefits of the suggested study include shorter prediction durations of 95.314, 122.530, and 131.205 s and lower model loss of 0.25%, 0.19%, and 0.15% for input sizes 10, 15, and 20, respectively. Three datasets had a reduced categorization error of 5.11% and an accuracy of 95.42%. In comparison to previous approaches, the work discussed here produces superior outcomes. RESEARCH HIGHLIGHTS: Recurrent convolutional neural network (RCNN) deep learning approach for optimizing time prediction and error classification in early melanoma detection. It extracts a high number of specific features from the skin disease image, making the classification process easier and more accurate. To reduce classification errors in accurately detecting melanoma, context dependency is considered in this work. By accounting for context dependency, the deprivation state is avoided, preventing performance degradation in the model. To minimize melanoma detection model loss, a skin disease image augmentation or regularization process is performed in this work. This strategy improves the accuracy of the model when applied to fresh, previously unobserved data.

Personality and the use of cancer screenings - Results of the German National Cohort

ObjectiveTo determine the association between personality characteristics and use of different cancer screenings. MethodsWe used data from the German National Cohort (NAKO; mean age was 53.0 years (SD: 9.2 years)) – a population-based cohort study. A total of 132,298 individuals were included in the analyses. As outcome measures, we used (self-reported): stool examination for blood (haemoccult test, early detection of bowel cancer), colonoscopy (screening for colorectal cancer), skin examination for moles (early detection of skin cancer), breast palpation by a doctor (early detection of breast cancer), x-ray examination of the breast (“mammography”, early detection of breast cancer), cervical smear test, finger examination of the rectum (early detection of prostate cancer), and blood test for prostate cancer (determination of Prostate-Specific Antigen level). The established Big Five Inventory-SOEP was used to quantify personality factors. It was adjusted for several covariates based on the Andersen model. Unadjusted and adjusted multiple logistic regressions were computed. ResultsA higher probability of having a skin examination for moles, for example, was associated with a higher conscientiousness (OR: 1.07, p < 0.001), higher extraversion (OR: 1.03, p < 0.001), higher agreeableness (OR: 1.02, p < 0.001), lower openness to experience (OR: 0.98, p < 0.001) and higher neuroticism (OR: 1.07, p < 0.001) among the total sample. Depending on the outcome used, the associations slightly varied. ConclusionsParticularly higher levels of extraversion, neuroticism and conscientiousness are associated with the use of different cancer screenings. Such knowledge may help to better understand non-participation in cancer screening examinations from a psychological perspective.

Skin Cancer Detection and Classification Using Neural Network Algorithms: A Systematic Review.

In recent years, there has been growing interest in the use of computer-assisted technology for early detection of skin cancer through the analysis of dermatoscopic images. However, the accuracy illustrated behind the state-of-the-art approaches depends on several factors, such as the quality of the images and the interpretation of the results by medical experts. This systematic review aims to critically assess the efficacy and challenges of this research field in order to explain the usability and limitations and highlight potential future lines of work for the scientific and clinical community. In this study, the analysis was carried out over 45 contemporary studies extracted from databases such as Web of Science and Scopus. Several computer vision techniques related to image and video processing for early skin cancer diagnosis were identified. In this context, the focus behind the process included the algorithms employed, result accuracy, and validation metrics. Thus, the results yielded significant advancements in cancer detection using deep learning and machine learning algorithms. Lastly, this review establishes a foundation for future research, highlighting potential contributions and opportunities to improve the effectiveness of skin cancer detection through machine learning.

Decoding the synergistic potential of MAZ-51 and zingerone as therapy for melanoma treatment in alignment with sustainable development goals.

Melanoma, an invasive class of skin cancer, originates from mutations in melanocytes, the pigment-producing cells. Globally, approximately 132,000 new cases are reported each year, and in South Africa, the incidence stands at 2.7 per 100,000 people, signifying a worrisome surge in melanoma rates. Therefore, there is a need to explore treatment modalities that will target melanoma's signalling pathways. Melanoma metastasis is aided by ligand activity of transforming growth factor-beta 1 (TGF-β1), vascular endothelial growth factor-C (VEGF-C)and C-X-C chemokine ligand 12 (CXCL12) which bind to their receptors and promote tumour cell survival, lymphangiogenesisand chemotaxis. (3-(4-dimethylaminonaphthelen-1-ylmethylene)-1,3-dihydroindol-2-one) MAZ-51 is an indolinone-based molecule that inhibits VEGF-C induced phosphorylation of vascular endothelial growth factor receptor 3 (VEGFR-3). Despite the successful use of conventional cancer therapies, patients endure adverse side effects and cancer drug resistance. Moreover, conventional therapies are toxic to the environment and caregivers. The use of medicinal plants and their phytochemical constituents in cancer treatment strategies has become more widespread because of the rise in drug resistance and the development of unfavourable side effects. Zingerone, a phytochemical derived from ginger exhibits various pharmacological properties positioning it as a promising candidate for cancer treatment. This review provides an overview of melanoma biology and the intracellular signalling pathways promoting cell survival, proliferationand adhesion. There is a need to align health and environmental objectives within sustainable development goals 3 (good health and well-being), 13 (climate action)and 15 (life on land) to promote early detection of skin cancer, enhance sun-safe practices, mitigation of environmental factorsand advancing the preservation of biodiversity, including medicinal plants. Thus, this review discusses the impact of cytostatic cancer drugs on patients and the environment and examines the potential use of phytochemicals as adjuvant therapy.

Artificial Intelligence Applied to Non-Invasive Imaging Modalities in Identification of Nonmelanoma Skin Cancer: A Systematic Review

Background: The objective of this study is to systematically analyze the current state of the literature regarding novel artificial intelligence (AI) machine learning models utilized in non-invasive imaging for the early detection of nonmelanoma skin cancers. Furthermore, we aimed to assess their potential clinical relevance by evaluating the accuracy, sensitivity, and specificity of each algorithm and assessing for the risk of bias. Methods: Two reviewers screened the MEDLINE, Cochrane, PubMed, and Embase databases for peer-reviewed studies that focused on AI-based skin cancer classification involving nonmelanoma skin cancers and were published between 2018 and 2023. The search terms included skin neoplasms, nonmelanoma, basal-cell carcinoma, squamous-cell carcinoma, diagnostic techniques and procedures, artificial intelligence, algorithms, computer systems, dermoscopy, reflectance confocal microscopy, and optical coherence tomography. Based on the search results, only studies that directly answered the review objectives were included and the efficacy measures for each were recorded. A QUADAS-2 risk assessment for bias in included studies was then conducted. Results: A total of 44 studies were included in our review; 40 utilizing dermoscopy, 3 using reflectance confocal microscopy (RCM), and 1 for hyperspectral epidermal imaging (HEI). The average accuracy of AI algorithms applied to all imaging modalities combined was 86.80%, with the same average for dermoscopy. Only one of the three studies applying AI to RCM measured accuracy, with a result of 87%. Accuracy was not measured in regard to AI based HEI interpretation. Conclusion: AI algorithms exhibited an overall favorable performance in the diagnosis of nonmelanoma skin cancer via noninvasive imaging techniques. Ultimately, further research is needed to isolate pooled diagnostic accuracy for nonmelanoma skin cancers as many testing datasets also include melanoma and other pigmented lesions.

Convolutional Neural Network Approach for Early Skin Cancer Detection

The field of medical image processing is rapidly adopting artificial intelligence. Its use is required for many applications in the healthcare industry. A machine can learn from experience without explicit programming thanks to computer education. It is an area within AI. Deep learning, a kind of machine learning, infers critical features for image processing via multiple layer processing and mathematical operations based on artificial neural networks. In the field of healthcare, which encompasses medicine and dentistry, artificial intelligence has several applications.Early melanoma skin cancer identification is necessary for effective therapy. Melanoma, among the various types of skin cancer, has recently gained international recognition as the most deadly one since it is much more likely to spread to other body regions if detected and treated quickly. Clinical diagnosis of various ailments is increasingly using non-invasive medical computer vision or medical image processing. These methods offer an automatic image processing tool that makes it possible to examine the lesion quickly and precisely. The procedures used in this study included building a database of dermoscopy images, preprocessing, segmenting using thresholding, extracting statistical features using asymmetry, border, colour, diameter, etc., and choosing features based on the total dermoscopy score, principal component analysis (PCA), and convocation neural network classification (CNN). According to the findings, a classification accuracy of 90.1% was attained.

Removal of Incidental Skin Cancer During Mohs Micrographic Surgery Indicated for a Different Primary Tumor.

Mohs surgery is a tissue-sparing, microscopically controlled procedure used to treat biopsy-proven skin cancers. Because Mohs surgery allows for examination of the complete margin of each tissue layer removed, separate cancers can be treated concomitantly when identified. As early detection of skin cancer is beneficial for reducing morbidity, incidental tumors discovered during Mohs surgery are of significant interest. Our objective was to determine the prevalence and characteristics of incidental skin cancers found during Mohs surgery. A retrospective chart review of cases seen at University of California, San Diego, from 2014 to 2021 was performed. Of 13,464 Mohs surgery cases, 4.53% ( n = 610) had incidental skin cancers found during removal of the initially identified tumor. Of the 610 cases, 88.4% ( n = 539) had basal cell carcinoma as the primary tumor and either squamous cell carcinoma (SCC) or SCC in situ as the incidental tumor. About 7.87% ( n = 48) had SCC as the primary tumor and basal cell carcinoma as the incidental tumor. All tumors were removed with clear margins and without significant complications. Diagnosis of incidental tumors during Mohs surgery enables early detection of skin cancer and circumvents the need for additional surgery, likely resulting in decreased morbidity and improved cost-effectiveness.

A conditional generative adversarial networks and Yolov5 Darknet -based skin lesion localization and classification using independent component analysis model

Automated diagnosis systems significantly improve the early detection of skin cancer, thereby contributing to its successful treatment. Skin lesion segmentation represents a crucial yet challenging issue within the realm of computer-assisted dermoscopy image analysis. Numerous segmentation techniques reliant on convolutional neural networks falter in achieving precise lesion boundaries due to the diminishing spatial scale of feature maps as they traverse through network layers. Nonetheless, neural networks demand a substantial amount of data, which presents a notable obstacle in the medical field. More data, and possibly a pre-trained model tailored to medical images, are imperative. Researchers have resorted to extensive augmentation and preprocessing methods, as well as fine-tuning the network using pre-trained models on unrelated images, in order to address the challenge of lesion segmentation as the initial step in skin cancer analysis. This arises from the fact that datasets are limited in size and encompass a diverse array of images, varying in terms of lighting, color, scale, and markings. The proposed method addresses the issue of precisely locating and classifying skin lesions in dermatological images. The architecture of Conditional Generative Adversarial Networks (cGAN) enables the generation of high-resolution, contextually pertinent images, thereby enhancing the discriminative properties of skin lesions. YOLOv5 Darknet has an effective object detection mechanism that aids in precise lesion localization. Independent component analysis (ICA), a signal processing technique that identifies statistically separate components from diverse data sources, is employed to improve the model's robustness and interpretability further. This facilitates extracting latent features from the combined dermatological images and data collection, thereby enhancing the model's understanding of complex lesion characteristics. According to experimental results on an extensive dermatological data set, the proposed cGAN-YOLOv5 technique is effective. In pinpointing and classifying lesion locations, the hybrid model outperforms the competition. The synergistic integration of cGANs, YOLOv5 Darknet, and ICA demonstrates the effectiveness of multifaceted integration in enhancing medical image analysis. Using high-resolution feature blocks and a noise-suppressing attention method, cGAN-YOLOv5 is proposed. Therefore, the proposed strategy segments skin lesions effectively.

Adaptive dermascopy application using machine learning

Skin cancer is the most lethal because skin cells develop abnormally. Finding skin cancer early is very important and may help stop some kinds of skin cancer, like melanoma and focal cell carcinoma. Early detection and classification of skin cancer are difficult and costly. Recurrent networks and ConvNets can automatically extract complex data. This paper proposes to use a handmade features-based multi-layer perceptron and a cascaded ensembled network to upgrade ConvNet models. This convolutional neural network model detects non-handmade picture qualities and generates features like color moments and material properties. With ensembled DL, accuracy increased from 85.3% with convolutional neural networks to 98.3%.

A deep learning outline aimed at prompt skin cancer detection utilizing gated recurrent unit networks and improved orca predation algorithm

Skin cancer is a risky ailment that can be effectively managed if detected promptly. However, timely recognition of skin cancer remains a challenge. In this study, a robotic computer-assisted tactic is proposed for the early detection of skin cancer. The motivation behind this research is to improve the accuracy and efficiency of skin cancer recognition. The proposed methodology involves a series of steps. Initially, the input images undergo preprocessing to enhance their quality and extract relevant features. These preprocessed images are then fed into a Gated Recurrent Unit (GRU) Network, a type of deep learning model known for its ability to capture sequential information. To optimize the performance of the GRU Network, we employ an enhanced variant of the Orca Predation Algorithm (OPA). This algorithm helps fine-tune the network parameters, improving its diagnostic capabilities. To validate and evaluate the effectiveness of our skin cancer diagnosis algorithm, we conducted experiments using the HAM10000 dataset, which contains a large collection of skin lesion images. We compared the results obtained from our proposed method, named GRU/IOPA, with eight existing techniques commonly used for skin cancer diagnosis. Five execution indices, namely sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy, were used to assess the performance of these methods. Our findings demonstrate that the GRU/IOPA system outperforms other existing methods in terms of sensitivity (0.95), specificity (0.97), PPV (0.95), NPV (0.96), and accuracy. These results indicate the effectiveness of our proposed method in diagnosing skin cancer compared to traditional approaches. The superior performance of GRU/IOPA highlights its potential impact on improving skin cancer diagnosis and reinforces its promise as an advanced tool in the field. In conclusion, our study presents a novel approach for timely skin cancer recognition through a robotic computer-assisted tactic. By utilizing the GRU/IOPA system, we achieve superior accuracy and efficiency in diagnosing skin cancer compared to existing techniques. This research offers significant contributions to the field of skin cancer diagnosis and opens up new avenues for future advancements in this area.

A Comparative Study of Machine Learning Models for Early Detection of Skin Cancer Using Convolutional Neural Networks

A Comparative Study of Machine Learning Models for Early Detection of Skin Cancer Using Convolutional Neural Networks

Two-Stage Input-Space Image Augmentation and Interpretable Technique for Accurate and Explainable Skin Cancer Diagnosis

This research paper presents a deep-learning approach to early detection of skin cancer using image augmentation techniques. We introduce a two-stage image augmentation process utilizing geometric augmentation and a generative adversarial network (GAN) to differentiate skin cancer categories. The public HAM10000 dataset was used to test how well the proposed model worked. Various pre-trained convolutional neural network (CNN) models, including Xception, Inceptionv3, Resnet152v2, EfficientnetB7, InceptionresnetV2, and VGG19, were employed. Our approach demonstrates an accuracy of 96.90%, precision of 97.07%, recall of 96.87%, and F1-score of 96.97%, surpassing the performance of other state-of-the-art methods. The paper also discusses the use of Shapley Additive Explanations (SHAP), an interpretable technique for skin cancer diagnosis, which can help clinicians understand the reasoning behind the diagnosis and improve trust in the system. Overall, the proposed method presents a promising approach to automated skin cancer detection that could improve patient outcomes and reduce healthcare costs.