## 2 Related Work  
In the realm of dermatological image diagnosis, significant advancements have been achieved by scholars and practitioners hailing from both domestic and international spheres. Notably, conventional machine learning methodologies have exhibited promising outcomes in delineating skin lesions through contour analysis. For instance, [PERSON] et al. [16] conducted a study based on the ABCDE criterion [17], encompassing the scrutiny of shape, edge regularity, texture, and color attributes of skin lesions. Leveraging image processing tools, they extracted quantitative features and employed SVM for classification, thereby evaluating the efficacy of the proposed framework. [PERSON] et al. [18] deployed the region-growing technique for lesion segmentation and feature extraction, followed by decision tree-driven classification, culminating in the selection of the random forest algorithm as the final model. Relative to SVM, random forest demonstrates diminished computational complexity, pliability in design, and proficiency in handling diverse categories of dermatological conditions. [PERSON] et al. [19], in their comparative investigation of melanoma classification, explored diverse data preprocessing methods, feature extraction strategies, and classification algorithms. Their findings underscored that employing linear normalization, HSV feature extraction, and a balanced random forest classifier yielded optimal results on the HAM10000 dataset, achieving an accuracy rate of 74.75%. Despite the strides made by assorted machine learning methodologies in skin classification, wherein some have surpassed the expertise of human practitioners in select cases [20], conventional machine learning algorithms, rooted in statistical principles, exhibit notable performance fluctuations across varied scenarios. Furthermore, these classical algorithms often necessitate intricate feature engineering and lack adaptability. Figure 1: Illustrative Instances of Diverse Skin Lesions from ISIC2018. In recent times, CNNs have garnered considerable traction in the realm of computer vision [21], eclipsing traditional machine-learning approaches in terms of classification and detection prowess. [PERSON] et al. [22] proposed a robust data augmentation technique adaptable to any deep-learning paradigm for skin lesion classification. Their EfficientNet0 methodology outperformed its counterparts on the ISIC2018 dataset, boasting an accuracy rate of 85.3%. [PERSON] et al. [23] conducted a comparative study of multiple models, affirming that DenseNet [24] exhibited superior performance in benign and malignant binary classification tasks, while EfficientNet [25] excelled in multi-classification endeavors. [PERSON] et al. [26] introduced the multi-level relationship capture network (MRCN), employing a region correlation learning module to emulate interrelations among distinct significant regions within the central lesion zone. Moreover, a cross-image learning module was employed to emulate profound semantic correlations across multiple images. Rigorous experiments across three arduous datasets validated the exceptional performance of MRCN. [PERSON] et al. [27] introduced DSCCNet, a deep learning architecture tailored for multi-classification diagnosis of skin cancer using dermoscopic imagery. Outperforming the baseline model, DSCCNet furnishes robust diagnostic assistance to dermatologists and medical practitioners. Nonetheless, CNNs confront certain constraints in skin lesion recognition, such as discerning analogous features, information loss, and constraints in capturing holistic, contextual information. While refinements in deep learning models and alternative methodologies exist to bolster recognition efficacy, sustained inquiry and enhancements are imperative to fortify accuracy and resilience. Consequently, sustained endeavors and exploration remain requisite in the realm of skin lesion identification. [PERSON] et al.