## 2 Related Work  
The majority of research on the classification of melanomas focuses on the use of the dermoscopic data, which provides more visual information and is frequently employed by professional dermatologists. Recent research on the CAD system for skin lesion categorization employs deep learning-based approaches. In most of the approaches, it is seen that the model requires more training time due to larger image size. Furthermore, the presently available public databases for skin lesion classification are mostly imbalanced, which hinders the performance of the model. To classify skin lesions, a study was performed by [PERSON] et al. [7] in which they employed a deep learning method based on vision transformers. A two-layer architecture is used in this work to accurately classify skin cancer. The transformer splits the augmented data into different patches and feeds the input to a multi-layer perceptron classifier to define its class with an accuracy of 96.14%. The study carried out by [PERSON] et al. [10] have pre-processed the HAM10000 database using different morphological operations. The handcrafted method for feature extraction is used to retrieve the features. The two-transfer learning models named EfficientNet-B0 and ResNet50V2 are used for skin lesion classification and obtained an accuracy of 94.9%. A research study by [PERSON] et al. [11] worked on the HAM10000 database by employing a multi-focus segmentation network (MFS-Net) based on a deep learning algorithm. The retrieval of deep features is performed using the parallel partial decoder technique to produce a segmentation map. Finally, two different attention modules are implemented to obtain a segmentation output. The authors achieved a dice score of 90.6% using the prescribed algorithm. [PERSON] et al. [12] used a transformer model based on a deep bottleneck. This model integrates the self-attention block to form a model with deep extracted features. It helped them to enhance the performance of overall categorization. The model accomplished a total accuracy of 96.1%. In the research work by [PERSON] et al. [13], a collective intelligence-based transfer learning system was presented. This system comprises of nine different transfer learning models. This individual model is trained using the HAM10000 database and the outputs of the individual network are combined using a decision-level fusion module. It helped them to boost their overall performance by 3%. This approach yields an accuracy of 86.71%. [PERSON] et al. [14] used multi-scale attention blocks which are a deep learning-based approach. This technique was implemented on the HAM10000 database to retrieve special features which will focus on skin lesion area. It has also adopted a loss weighting which helped to solve the issue of imbalanced data per class. The performance of this model gains an accuracy of 91.6%. The study stated in [15] is utilized for the categorization of skin lesions using the HAM10000 dataset. Multi-Scale Multi-CNN (MSM-CNN), a DL model built on a three-tier ensemble approach, was employed in this work. The proposed model results are then compared to the pre-trained CNN models such as EfficientNetB0, SeResNeXt-50, and EfficientNetB1. The MSM-CNN achieves the highest accuracy of 96.3% compared to other models. [PERSON] et al. [16] used a concatenation of Xception and ResNet50 models on the HAM10000 database. A sliding window method is implemented for the purpose of training as well as testing the system. The presented approach yields a good accuracy of 97.8% on testing data. In the study article [17], a classification of skin lesions is performed using the fusion of handcrafted and DL-based features and is further classified using ML classifiers to achieve an accuracy of 92.4%. Through these related work studies we have identified some shortcomings which have been overcome by our proposed model. We can clearly see that there is a scope to improvise the performance metrics in terms of accuracy.