# Fuzzy Logic with Deep Learning for Detection of Skin Cancer
## 4 Results and Discussion
### Analysis of Results
This can be attributed to the highly specialized pre-processing steps and novel segmentation methods employed, which contribute to the exceptional evaluation metric scores. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Acc (\%)** & **Sen (\%)** & **Spe (\%)** & **Jac (\%)** & **Dic (\%)** \\ \hline ISIC 2018 & 95.91 & 91.62 & 97.36 & 85.00 & 91.89 \\ ISIC 2017 & 96.16 & 91.88 & 98.26 & 88.73 & 94.03 \\ PH2 & 96.50 & 97.50 & 96.25 & 84.78 & 91.76 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Segmentation results of second phase. Figure 10: (**a**) Initial image input. (**b**) Lesion after undergoing hair removal and image enhancement pre-processing. (**c**) Result of segmentation after the first phase. (**d**) Result of segmentation after the second phase. (**e**) Detection and bounding box formation of melanoma lesion using the YOLO classifier. The proposed work is compared with several inspiring and successful segmentation methods for the PH2 dataset, including multi-staged fully convolutional network (FCN) with parallel integration (mFCN-PI) [37], semi-automated grab cut algorithms [38], semantic segmentation techniques [39], and synchronized segmentation with classification methods using a bootstrapping convolutional neural network model [40]. [PERSON] et al. have integrated MaskRCNN and RetinaNet for the segmentation of lesions [41]. However, our proposed method achieves an accuracy score of 98.50%, surpassing most of the well-known approaches. Additionally, for the remaining four evaluation parameters, the proposed method achieves the highest scores of 97.50% for sensitivity, 98.75% for specificity, 92.86% for Jaccard score, and 96.30% for Dice score. For the ISIC 2017 dataset, the proposed algorithm is compared with several models including deep convolutional deconvolution neural network (CDNN) [42], fully convolutional network (FCN) [43], a fully convolutional-residual network (FCRN) [44], segmentation by assembling crowdsourced results of ISIC 2017 challenge [45], transfer learning with pre-trained VGG16 or ResNet50 [46], a fully convolutional network (FCN) architecture with ResNet18 and AlexNet in encoder and three deconvolution layers in decoder part [47], and FrCN model for simultaneous segmentation and classification [48]. Among these, the accuracy performance of [PERSON] et al. [39] is the second-best in the table with a score of 95.3%, while our proposed method achieves an accuracy score of 96.17%. In the case of ISIC 2018 dataset, the proposed method is compared with several works including Encoder-Decoder with pyramid pooling modules [49], DilatedSkinNet architecture [50], encoder-decoder network with CRF [51], and Encoder-decoder algorithm with Deeplab and PSPNet [52]. The comparative study reveals that the proposed algorithm achieves the highest scores for accuracy, Jaccard index, and dice coefficient, which are 95.9%, 85.00%, and 91.9% respectively. The specificity score for the ISIC 2018 dataset is 97.4%, which is similar to that of [PERSON] et al. [49]. Our novel method for segmentation of lesion using fuzzy logic proved to be effective and quick when compared with other deep learning method which employs complex architecture of ResNet34, deep convolutional neural network (DCNN), autoencoders, and fully convolutional neural networks (FRCN).