# Fuzzy Logic with Deep Learning for Detection of Skin Cancer
## 4 Results and Discussion
### Analysis of Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**SOTA** & **Year** & **Ac (\%)** & **Se (\%)** & **Sp (\%)** & **JJ (\%)** & **Dc (\%)** \\ \hline Proposed method & 2021 & **95.9** & 91.6 & 97.4 & 85.0 & **91.9** \\ [PERSON] [41] & 2023 & \multicolumn{1}{c}{****————} & 91.6 & **97.8** & **91.4** & 90.7 \\ [PERSON] et al. [47] & 2022 & \multicolumn{1}{c}{****———} & 94.65 & **95.85** & 87.86 & 84.17 & 91.40 \\ [PERSON] et al. [49] & 2019 & \multicolumn{1}{c}{****————} & 90.2 & 97.4 & 83.7 & 90.3 \\ [PERSON] et al. [50] & 2018 & 94.3 & 91.8 & 96.4 & 83.4 & 90.0 \\ [PERSON] et al. [51] & 2018 & 94.5 & 94.0 & 94.2 & 87.7 & 90.3 \\ [PERSON] et al. [52] & 2018 & 94.2 & 90.6 & 96.3 & 83.8 & 89.8 \\ \hline \end{tabular}
\end{table} 
Table 7: Comparison of the segmentation results obtained from the proposed method with a recent study conducted on the ISIC 2018 dataset. \begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Classifier** & **Method** & **Ac (\%)** & **Se (\%)** & **Sp (\%)** & **Pr (\%)** & **AUC** & **Time** \\ \hline \multirow{3}{*}{TREE} & CT & 91.5 & 87.82 & 93.30 & 86.5 & 0.95 & 8.79 \\  & ST & 88 & 88.33 & 87.84 & 78.03 & 0.92 & 12.65 \\ \hline \multirow{3}{*}{SVM} & LSVM & 96.5 & 93.91 & 97.77 & 95.36 & 0.96 & 11.21 \\  & CSVM & 86.5 & 85.28 & 87.10 & 76.36 & 0.91 & 142.76 \\  & QSVM & 97 & 94.42 & 98.26 & 96.37 & 0.97 & 21.41 \\  & MGSVM & 96.33 & 94.92 & 97.02 & 93.97 & 0.98 & 14.64 \\ \hline \multirow{3}{*}{KNN} & FKNN & 93.83 & 92.39 & 94.54 & 89.22 & 0.91 & 10.45 \\  & MKNN & 97.33 & 94.42 & 98.76 & 97.38 & 0.97 & 10.1 \\ \cline{1-1}  & Cosine & 97 & 93.91 & 98.51 & 96.86 & 0.98 & 11.02 \\ \cline{1-1}  & Cubic & 96.66 & 93.40 & 98.26 & 96.34 & 0.97 & 101.42 \\ \cline{1-1}  & WKNN & 98.16 & 96.45 & 99.01 & 97.94 & 0.98 & 12.94 \\ \hline YOLO & PM & **98.16** & 95.43 & 99.50 & **98.95** & **0.99** & **7.01** \\ \hline \end{tabular}
\end{table} 
Table 8: Contrast between the modified YOLO classifier and other widely recognized classifiers. We have benchmarked the proposed method with classical method such as Tree, SVM, and KNN for classification of melanoma lesion. The pre-processed and segmented lesion images are passed through to each classifier for classification of lesion. We have employed classical algorithms which can handle noisy data and outliers. As is known, decision trees can handle noisy features by creating multiple splits and finding alternative paths. Similarly, SVMs can also handle noisy data by using a soft-margin approach that allows for some misclassification. This robustness is advantageous for melanoma detection as the data quality is noisy and irregular. On the other hand, deep learning models are sensitive to noisy data. Moreover, the classical methods are interpretable and valuable for medical applications. Additionally, the availability of training limited data has obligated us to employ classical methods as they perform well will limited data without a fear of exploding or vanishing gradient. The comparison among various classifiers and our suggested method has clearly demonstrated improved efficiency  
adversarial networks (GAN) for the task of high-quality synthetic data generation (which will increase the size of dataset and thereby increase the accuracy of classification). We also aim add a clinical feature extraction method (based on principles of ABCD rule) which will help decrease the specificity and assure the medical practitioners to use our algorithm.