# A Probabilistic-Based Deep Learning Model for Skin Lesion Segmentation
###### Abstract  
The analysis and detection of skin cancer diseases from skin lesion have always been tedious when done manually. The complex nature of skin lesion images is one of the key reasons for this. The skin lesion images contain noise and artifacts such as hairs, oil and bubbles, blood vessels, and skin lines. They also have variegated colors, low contrast, and irregular borders. Various computational approaches have been designed in the past for aiding in the detection and diagnosis of skin cancer diseases using skin lesion images. The existing techniques have been limited due to the interference of the aforementioned features of skin lesion. Recently, machine learning techniques, in particular the deep learning techniques have been used for the detection of skin cancer. However, they are still limited to the fuzzy and irregular borders of skin lesion images coupled with the low contrast that exists between the diseased lesion and healthy tissues. In this paper, we utilized a probabilistic model for the enhancement of a fully convolutional network-based deep learning system to analyze and segment skin lesion images. The probabilistic model employs an efficient mean-field approximate probabilistic inference approach with a fully connected conditional random field that utilizes a Gaussian kernel. The probabilistic model further performs a refinement of skin lesion borders. The whole framework is tested and evaluated on publicly available skin lesion image datasets of ISBI 2017 and PH2. The system achieved a better performance, having an accuracy of 98%. Keywords:fully convolutional networks; Gaussian kernel; conditional random fields; probabilistic model; skin cancer; segmentation +
Footnote †: journal:  
==WARNING: Truncated because of repetitions==
The Jaccard Index and dice coefficient define the area of overlap that is calculated between the segmented binary image and ground truth label. The average Jaccard Index value of all the images evaluated to 90.50% using the enhanced method. This achieves a better performance than existing methods as shown in Table 3.  
In addition, the results of the enhanced system was also compared with the existing methods as shown in Table 3 using the Jaccard Index and Table 4, using metrics such as accuracy, dice-coefficient, sensitivity, and specificity. In the two tables, the enhanced system outperforms the existing state-of-the-arts in all the evaluation metrics. This shows that the proposed system is more reliable and efficient than some of the existing systems. Some pretrained model have been used for the benchmark of the proposed model. The pretrained models have been evaluated on the ISBI 2017 dataset and their performance evaluated against the proposed model. The proposed model outperforms the pretrained model as shown in Table 5. Lastly, the proposed model was evaluated on the PH2 dataset and the results are shown in Table 6 for the deep learning model and enhanced model. Lastly, the proposed system utilizes a light-weight framework with a reduced number of parameters when compared with existing methods as shown in Table 7. This also minimizes the computational resources requirements. +++ \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **Accuracy** & **Dice Score** & **Sensitivity** & **Spec** \\ \hline Enhanced deep learning model & 96.0 & 93.0 & 94.0 & 96.0 \\ Deep learning model & 95.0 & 92.0 & 93 & 95 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance analysis (%) and comparison of the enhanced system with the method without enhancement on the PH2 datatest. Figure 4: The figure shows the performance of the deep learning model against the enhanced method on ISBI 2017 skin lesion images. Figure 5: The figure shows the segmentation outcomes of the system on the ISBI 2017 dataset: The first row represents the input images; the second rows represents the ground truth labels; the third row represent segmented results on the deep learning model; and the last row shows the segmented results of the enhanced system.