## 6 Conclusions  
This work was conducted based on the problem revolving around the doubtful diagnosis of skin anomalies, namely NV and two types of skin cancer, BCC and MEL. Using the selected methodology, VGG16, three models were developed, Model 1, Model 2, and Model 3, with diagnostic validation accuracies of 86%, 94%, and 93%, respectively. These values are satisfactory. We found that the second model is the best of the three by comparing these values. We used validation accuracy for comparison purposes. It is not advisable to use it to determine the performance of a model. To learn more about the diagnostic behavior and performance of Model 2, it was tested on an additional unpublished dataset of 4500 photos. After the learning phase, the following results were obtained: 84%, 84.5%, 84.6%, and 86% for the accuracy, f1 score, recall, and accuracy, respectively. We found that the model works well when comparing these results with similar work, particularly by comparing the precision values. In this study, we also varied the quantity of the data for each model in order to understand how these changes impact the model's performance. During the comparison phase between the three models, we kept the same code base. This means that the variables used during the training and validation phases, which could affect the model's performance, were consistent for all three models. This approach ensured a fair comparison, with the results being influenced only by a single variable, which is the amount of data used to form each model. Model 1 was formed with 14,454 images, the second with 13,232 images, and the third with 10,232 images. The images in Models 2 and 3 come from a single dataset, while Model 1 was trained with another dataset to observe the results. The comparison step demonstrated the critical importance of utilizing high-quality images to develop a successful model. By highlighting this in training profound learning models for the classification of medical images, our work contributes to the field of deep learning models. Indeed, these models require a substantial amount of data, which has prompted extensive research efforts to collect additional data in pursuit of improved performance. However, a significant challenge remains due to the considerable lack of data in most disciplines, particularly in the medical field. In summary, to achieve the desired results, researchers must place greater emphasis on the preprocessing steps of the images. This research shows the effectiveness of the VGG-16 model in skin cancer diagnosis, and several avenues for future research could enhance its performance and applicability. Exploring alternative architectures, such as ResNet and VGG-19, may improve accuracy and robustness due to their advanced feature extraction capabilities. Incorporating ensemble methods that combine predictions from multiple models could further enhance diagnostic accuracy. However, it is crucial to note that these models must be trained on pre-processed data to ensure optimal performance. Future research should focus on expanding the dataset to include a wider variety of skin types, lesions, and imaging conditions to improve generalizability. Implementing techniques such as data augmentation and synthetic image generation could simulate diverse scenarios and strengthen model adaptability. Additionally, integrating explainable AI methods would provide insights into the model's decision-making processes, increasing trust among healthcare professionals. Lastly, conducting longitudinal studies and real-world clinical trials to evaluate the model's performance over time and in diverse healthcare settings would be invaluable for assessing its practical utility and efficacy in improving patient outcomes. In the future, it may be possible to achieve better results using other deep learning networks like ResNet, VGG-19, and RCNN. Our work was conducted using the VGG-16 model, which is regarded as an effective architecture.