# Aspects of Lighting and Color in Classifying Malignant Skin Cancer with Deep Learning
## 4 Conclusions and Future Work  
Deep learning achieves good results in skin cancer classification, but this methodology also attracts interest in solving problems related to image acquisition. Techniques developed \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Works** & **Acc (\%)** & **P (\%)** & **Comments** \\ \hline [PERSON] et al. [5] & 72.3 & 69 & Result achieved with ResNet50. Data augmentation focusing on dual estimation of lighting. \\ [PERSON] et al. [38] & 85 & 86 & Better results with ResNet50. Correction using statistical methods, focusing on global lighting adjustments. \\ [PERSON] et al. [16] & 79.2 & - & Results achieved with DenseNet121. It uses GAN for lighting and color correction, highlighting the potential of this technique in segmentation and classification. Precision data not specified. \\ Proposed approach & 91.3 & 92.3 & Results were determined by ResNet50, using the LCDPNet correction technique. \\  & 86.5 & 87.9 & Better results with DenseNet121. Application of the DSN correction technique. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance comparison with similar works. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Models** & \multicolumn{2}{c}{_P(\%)_} \\ \hline  & **LCDPNet** & **LLNeRF** & **DSN** \\ \hline InceptionV3 & \(1.8\pm 0.046\) & \(-1.1\pm 0.046\) & \(-2.3\pm 0.040\) \\ Xception & \(0.6\pm 0.094\) & \(2.6\pm 0.018\) & \(-2.0\pm 0.071\) \\ ResNet50 & \(2.2\pm 0.050\) & \(0.5\pm 0.027\) & \(1.0\pm 0.036\) \\ DenseNet121 & \(-0.9\pm 0.051\) & \(1.7\pm 0.048\) & \(3.4\pm 0.077\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Differences between noncorrection and application of lighting and color correction methods. for this purpose can automatically adjust lighting and color in digital images. Observing and adjusting these characteristics can significantly improve classification results in the automatic detection of skin cancer. This study emphasizes the importance of lighting and color in classifying malignant skin cancer using deep neural network architectures. We demonstrated that applying advanced correction techniques, such as LCDPNet, LLNeRF, and DSN, can increase the precision and accuracy of these networks, achieving improvements of between 3 and 4% in precision. The models that used images corrected with the LCDPNet and LLNeRF techniques showed improvements in most classification metrics. Although these techniques are not specific to images of skin lesions, they enhanced the precision in diagnosing malignant skin cancer. We also noted that the architectures used for classification achieved different results by applying lighting and color correction techniques. The Xception and DenseNet121 models proved more robust, while InceptionV3 and ResNet50 were more sensitive to these changes. One explanation is that color characteristics can change after convolution operations, causing the architecture to focus more on structural information and reducing the emphasis on colors. Factors such as the size of the kernels, the number of layers, and pooling operations can increase or decrease the importance of color in optimizing a loss function. Proper preprocessing of lighting and color aspects and a fine-tuned classification model can improve the identification of malignant skin cancer. For future work, we recommend using other lighting and color correction methods, such as methods based on self-supervised learning, attention mechanisms, and style transfer, to test the sensitivity of other, more modern deep architectures. Another suggestion is to check and compare this sensitivity with other conventional techniques, such as histograms, white balance, color temperature, and chromatics. New methodologies of this type would provide valuable information about the capabilities of these lighting and color correction techniques in skin cancer images.