## 6 Conclusions and Future Scope  
Melanocyte cells are responsible for the formation of pigmented lesions. Skin malignancies such as melanoma are caused by the unregulated division of melanocyte cells, which may have a damaging effect on the human body. The dermatologists with extensive training interpret dermoscopic images. Due to the insufficiency of educated specialists and the need to minimize human-induced mistakes, the use of computer-assisted systems is emphasized. Convolution neural network, a method for deep learning that retrieves features from images, achieved huge success in the domain of computer vision. The preprocessing with ESRGAN helps us to reduce the size of images with better resolution and overall execution time for the experiment. The complexion in the model in terms of image shape that it considers as an input leads to maximum execution time in training the model. Hence, in this work, we have used images with a resolution of 28 \(\times\) 28 pixels. Before resampling the images, the original images were enhanced using the ESRGAN dataset which helps to preserve the eminent features in the input images after down sampling. In this experimental analysis, we have implemented a HAM10000 dataset having 10,015 images of skin lesions and are categorized into seven different classes using a custom CNN model. The experimental model achieved accuracies of 98.77%, 98.36%, and 98.89% for protocol-I, protocol-II, and protocol-III, respectively, and it is seen to be competitively high as compared to the pretrained models presented by different researchers.  
In the future, our aim is to work on the diagnosis of real-time skin lesions with improvement in the testing accuracy. We also hope to implement our proposed model to work on larger datasets if available for skin-cancer image categorization. It will in turn help us to enhance the performance metric scores. It is anticipated that the proposed work will help the dermatologist to examine and classify the class of skin cancer in lesser time duration and with more precision. Additionally, it will assist in reducing the total costs associated with skin cancer diagnosis. There is a scope for further enhancement in performance metrics such as accuracy, precision, and recall.  
This research received no external funding.  
Not applicable.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \begin{tabular}{c} **Sr. No.** \\ \end{tabular} & \begin{tabular}{c} **Work** \\ \end{tabular} & \begin{tabular}{c} **Data Augmentation/Balancing? (Yes/No).** \\ **Total Number of Images after Data** \\ **Augmentation/Balancing** \\ \end{tabular} & \begin{tabular}{c} **Methodology** \\ \end{tabular} & \begin{tabular}{c} **Accuracy (\%)** \\ \end{tabular} \\ \hline \multirow{3}{*}{1} & [PERSON] et al. [2] & Yes, & \
\begin{tabular}{c} Lightweight \\ Dynamic Kernel \\ Deep-Learning-Based \\ Convolutional Neural Network \\ \end{tabular} & \multirow{3}{*}{97.8\%} \\ \cline{1-1} \cline{3-4}  & [PERSON] et al. [39] & No & S2C-DeLeNet & 90.58\% \\ \cline{1-1} \cline{3-4}  & Proposed algorithm &
\begin{tabular}{c} Yes, \\ 46,935 \\ \end{tabular} & ESRGAN-CNN & 98.89\% \\ \hline \hline \end{tabular}
\end{table}
Table 12: Highest accuracy for Protocol-III.