# Skin Cancer Classification Using Fine-Tuned Transfer Learning of DENSENET-121
## 4 Results and Discussion
### Statistical Significance  
The statistical significance to be used in this study are mean Accuracy, Standard Deviation Accuracy, and Confidence Interval. Mean accuracy is the average performance of the model over multiple tests. If one tests one's model several times, mean accuracy tells one, on average, how often the model correctly predicts the outcome. \[\text{Mean Accuracy}=\frac{1}{N}\sum_{i=1}^{N}\text{Accuracy}_{i} \tag{7}\]  
where the following definitions are used:  
* \(N\) is the number of tests or experiments. * \(\text{Accuracy}_{i}\) is the accuracy obtained in the \(i\)-th test. * \(\frac{1}{N}\) averages the sum of accuracies to provide the mean. Confidence interval gives one a range of values within which one can be fairly sure the true accuracy of the model lies. It is like saying we are 95% confident that the model's true accuracy is between these two numbers. \[\text{CI}=\text{Mean Accuracy}\pm t\times\left(\frac{\text{Standard Deviation}}{\sqrt{N}}\right) \tag{8}\]  
where the following definitions are used:  
* Mean Accuracy is the average accuracy calculated previously. * \(t\) is the t-score (or z-score) for the desired confidence level (e.g., 1.96 for a 95% confidence level). * Standard Deviation is the measure of variability in accuracy. * \(\sqrt{N}\) is the square root of the number of tests, scaling the standard deviation to reflect sample size. Standard deviation of accuracy measures how much the accuracy varies across different tests. It tells one whether the model's performance is consistent or if it fluctuates a lot. \[\text{Standard Deviation}=\sqrt{\frac{1}{N-1}\sum_{i=1}^{N}(\text{Accuracy}_{ i}-\text{Mean Accuracy})^{2}} \tag{9}\]where the following definitions are used:  
* Mean Accuracy is the average accuracy calculated previously. * \(N\) is the number of tests or experiments. * Accuracy\({}_{i}\) is the accuracy obtained in the \(i\)-th test. * The term \(\frac{1}{N-1}\) normalizes the sum of squared deviations to account for sample size. Fine-tuned VGG16: The mean accuracy of 0.87 indicates that, on average, the model correctly classifies 87% of the test samples. The standard deviation of 0.01 reflects very little variability in its performance across different experiments. The 95% confidence interval, ranging from 0.86 to 0.87, suggests that if we repeated the experiments multiple times, the true accuracy would fall within this range 95% of the time, as shown in Table 11.  
Fine-tuned ResNet-34: With a mean accuracy of 0.79, RESNET correctly identifies 79% of the test samples on average. The standard deviation of 0.02 shows slightly more variation in performance across tests. The confidence interval between 0.78 and 0.80 indicates that we can be 95% confident the true accuracy lies within this narrow range, as shown in Table 11.  
Fine-tuned DenseNET-121: This model also achieves a mean accuracy of 0.87, meaning it performs similarly to VGG16 by correctly classifying 87% of the samples on average. The standard deviation of 0.02 suggests some minor variability. The 95% confidence interval from 0.86 to 0.87 confirms consistent performance, with the true accuracy likely falling within this range, as shown in Table 11.  
Fine-tuned Inception_v3: With a mean accuracy of 0.82, this model correctly classifies 82% of the samples on average. The standard deviation of 0.03 indicates greater variability in results. The confidence interval of 0.81 to 0.83 suggests a moderate range where the true accuracy is likely to be found, as shown in Table 11.  
Fine-tuned EfficientNETB0: Achieving a mean accuracy of 0.85, this model correctly identifies 85% of the samples on average. The standard deviation of 0.012 indicates very consistent performance across different tests. The confidence interval, from 0.84 to 0.85, suggests that the true accuracy is tightly confined within this range as shown in Table 11.  
CNN: With a mean accuracy of 0.83, the CNN correctly classifies 83% of the samples on average. The standard deviation of 0.03 indicates some variability.