# Aspects of Lighting and Color in Classifying Malignant Skin Cancer with Deep Learning
## 3 Results and Discussions  
Following the experimental methodology, we developed four distinct scenarios to test the performance of classification models under different conditions. These scenarios include testing the models' performance without any lighting and color correction, applying LCDPNet, using LLNeRF for corrections, and applying the DSN technique. For comparison purposes, we used the models' performance in classifying images without the intervention of any correction techniques as a reference. Table 2 displays the results achieved in all suggested scenarios. When analyzing Table 2, we noticed that the InceptionV3, even without adjustments for lighting and color, achieves good performance, with a precision of 91.4%. The ResNet50 and DenseNet121 perform similarly, but the ResNet50 stands out in accuracy and _AUC_. On the other hand, the Xception exhibits lower metrics under the same conditions compared to the different models. When applying the LDCPNet correction technique, we observed a growth trend in the results for most of the analyzed models compared to the scenario without correction. This technique increases the precision of the ResNet50 and InceptionV3 models to 93.2% and 92.3%, respectively. However, we observed a decrease in the _AUC_ of the ResNet50, which might indicate a loss in its ability to make accurate predictions. On the other hand, the DenseNet121 does not show significant improvements in the metrics, suggesting that the correction did not benefit the model. Applying the LLNeRF method for correction improved metrics across all evaluated models compared to the scenario without correction. We noted an increase in the accuracy of InceptionV3 from 89.3% to 91.8%. However, precision and _AUC_ saw a slight decrease. In contrast, Xception and DenseNet121 displayed significant increases in all metrics. Highlighting all results after this correction, the ResNet50 was the model that showed the most significant performance gains. When using the DSN correction technique, we observed varied results. InceptionV3 decreased accuracy, from 91.4% to 89.1%, and in _AUC_, from 91.8% to 89.6%. Xception also experienced a significant reduction in accuracy, dropping from 85.6% to 81.6%. Conversely, ResNet50 and DenseNet121 improved metrics with increased accuracy rates and _AUC_. Notably, the accuracy of ResNet50 increased from 89.9% to 93.1%. To make the variations between the analyzed models without lighting correction and the evaluated correction techniques clearer, Table 3 presents the specific difference values and weighted standard deviation. This approach allows for a quantitative comparison of the impact of each correction technique. \begin{table}
\begin{tabular}{c c c c} \hline
**Models** & _Acc (\%)_ & _P (\%)_ & _AUC (\%)_ \\ \hline \multicolumn{4}{c}{No Lighting and Color Correction} \\ \hline InceptionV3 & 89.3 & 91.4 & 91.8 \\ Xception & 87.8 & 83.6 & 85.6 \\ ResNet50 & 89.9 & 90.1 & 82.6 \\ DenseNet121 & 82.6 & 84.5 & 81.6 \\ \hline \multicolumn{4}{c}{LCDPNet} \\ \hline InceptionV3 & 90.1 & 93.2 & 90.6 \\ Xception & 86.5 & 84.2 & 85.3 \\ ResNet50 & 91.3 & 92.3 & 84.3 \\ DenseNet121 & 81.2 & 83.6 & 84.6 \\ \hline \multicolumn{4}{c}{LLNeRF} \\ \hline InceptionV3 & 91.8 & 90.3 & 89.9 \\ Xception & 89.3 & 86.2 & 88.5 \\ ResNet50 & 92.9 & 90.6 & 90.3 \\ DenseNet121 & 84.6 & 86.2 & 89.3 \\ \hline \multicolumn{4}{c}{DSN} \\ \hline InceptionV3 & 88.2 & 89.1 & 89.6 \\ Xception & 86.8 & 81.6 & 84.3 \\ ResNet50 & 93.1 & 91.1 & 83.1 \\ DenseNet121 & 86.5 & 87.9 & 83.1 \\ \hline \end{tabular}
\end{table} 
Table 2: Classification results with all developed scenarios. The lighting and color correction techniques LCDPNet, LLNeRF, and DSN enhanced the effectiveness of the models in the majority of the experiments. We observed an increase in precision rates in detecting cancer, achieving an average gain of approximately 3.4%. However, it is important to highlight that each model exhibited distinct behavior when dealing with these specific characteristics.