# 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 \hline
**SOTA** & **Year** & **Ac (\%)** & **Se (\%)** & **Sp (\%)** & **JI (\%)** & **Dc (\%)** \\ \hline Proposed method & 2021 & **96.17** & **91.88** & 98.26 & **88.73** & **94.03** \\ [PERSON] et al. [47] & 2022 & 93.47 & 79.25 & 97.8 & 77.54 & 87.35 \\ [PERSON] et al. [39] & 2020 & 95.3 & 87.5 & 88.5 & ---- & ---- \\ [PERSON] et al. [48] & 2020 & 81.57 & 75.67 & 80.62 & ---- & ---- \\ [PERSON] et al. [46] & 2019 & 94.95 & 85.87 & 95.66 & 78.92 & 88.12 \\ [PERSON] et al. [44] & 2018 & 93.2 & 82 & 97.8 & 76.2 & 84.7 \\ [PERSON] et al. [45] & 2018 & 93.6 & 81.6 & 98.3 & 78.2 & 87.8 \\ [PERSON] et al. [42] & 2017 & 93.4 & 82.5 & 97.5 & 76.5 & 84.9 \\ [PERSON] et al. [43] & 2017 & 93.4 & 80.2 & **98.5** & 76.0 & 84.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of the segmentation results obtained from the proposed method with a recent study conducted on the ISIC 2017 dataset. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**SOTA** & **Year** & **Ac (\%)** & **Se (\%)** & **Sp (\%)** & **JI (\%)** & **Dc (\%)** \\ \hline Proposed method & 2021 & 98.50 & **97.50** & **98.75** & 92.86 & **96.30** \\ [PERSON] [41] & 2023 & ---- & 93.20 & 96.10 & **92.90** & 94.3 \\ [PERSON] et al. [39] & 2020 & **98.70** & 92.90 & 96.90 & ---- & ---- \\ [PERSON] et al. [40] & 2020 & 96.50 & 96.70 & 94.60 & 89.40 & 94.20 \\ [PERSON] et al. [38] & 2019 & 92.99 & 83.63 & 94.02 & 79.40 & 88.13 \\ [PERSON] et al. [37] & 2019 & 95.03 & 96.23 & 94.52 & 85.90 & 92.10 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of the segmentation results obtained from the proposed method with a recent study conducted on the PH2 dataset. The segmentation of melanoma lesion is difficult due to its asymmetrical shape, irregular border, and varied colour. We have not employed deep learning models for the segmentation of lesions as they are computationally expensive and require significant computational resources, whereas threshold based fuzzy logic for segmentation of lesion is computationally lightweight and is thus more efficient for real-time or resource-constrained environments. Moreover, fuzzy logic-based methods can handle noise and uncertainties in the image data effectively. By incorporating fuzzy sets and membership functions, we can account for variations and imperfections in the input image. Deep learning models, on the other hand, are more sensitive to noisy or imperfect input data. We compared our proposed recognition algorithm with several well-established classifiers, namely decision tree, Support Vector Machine (SVM), k-nearest neighbor (KNN), and YOLO v3. The comparison between our method using YOLO and these classifiers was conducted using various metrics. In addition to metrics such as sensitivity, specificity, precision, accuracy, and area under curve (AUC), we also considered the time taken (in seconds) as a measure to assess the speed of our method. For the same set of datasets, we employed different variants of SVM, including Lagrangian Support Vector Machine (LSVM), Consensus Support Vector Machine (CSVM), Quantum Support Vector Machine (QSVM), and Medium Gaussian Support Vector Machine (MGSVM), to classify malignant lesions. Similarly, we utilized various methods of KNN, such as Fuzzy k-nearest neighbor (FKNN), Modified k-nearest neighbor (MKNN), Weighted k-nearest neighbor (WKNN), Cosine KNN, and Cubic KNN. Despite using similar datasets for the classification of melanoma lesions, our YOLO classifier outperformed the others in terms of accuracy and AUC value. Furthermore, it accomplished the classification task in just 7.01 s. Table 8 presents a tabular representation of the different classifiers, along with the various methods used and the evaluation parameters.