# A Decision Support System for Melanoma Diagnosis from Dermoscopic Images
## 7 Discussions  
Researchers have been keen to develop an efficient and high-performing CAD for medical use. Lower computation cost, real-time processing, and open architecture are also welcome characteristics.  
The choice of the database used as a test-bench is crucial, as the performance achieved by a procedure usually depends on it. The PH2 database adopted by the authors provides a challenging bench-test because some of its images are dimly lit, contain hairs, are connected to the boundary edge of the provided image, or occupy almost the entire image area.  
In several studies in the literature, some of the images from the PH2 database have been discarded without providing a detailed description of image-selection criteria [47; 48; 49; 50; 51]. The results reported in these papers are consequently difficult to verify, their method cannot be reproduced, and performance comparisons with other studies are difficult. We are of the opinion that the number of images used and/or discarded should always be reported.  
Image quality in the chosen database is inconsistent; hence, a subset of low-quality images has been identified to assess the impact of image quality on the performance of CAD systems. Denoting by ND the number of poor-quality images to be discarded from the dataset (\(0\leq\text{ND}\leq 49\)), a statistic of the method performance with regard to low-quality images is extracted. The study is carried out varying ND within the previously defined range, and a statistic of the performance measurement metrics is accumulated for every ND. For each value of the ND parameter, the approach is repeated randomly, changing the selected low-quality images over thousands of different choices. For the sake of comparison, precision and specificity are considered performance metrics.  
To simply compare the results obtained with different selections of the images to be discarded, the k-means technique is used with k = 2.  
In Figure 7, the boxplot of the performance achieved with ND equal to 20 is shown.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Technique** & **Se (\%)** & **Sp (\%)** & **Ac (\%)** \\ \hline SVM [26] & 97.90 & - & 98.20 \\ \hline YOLO [27] & 97.5 & 97.5 & 97.5 \\ \hline TDS index [28] & 60.5 & 89.5 & 84 \\ \hline SVM [29] & 100 & 87.5 & 92.5 \\ \hline Dynamic selection [30] & 97 & 100 & 97 \\ \hline Associative model [31] & 94.4 & 98.7 & 97.8 \\ \hline MobileNet/DenseNet [32] & 100 & 96.88 & 97.5 \\ \hline Neural Network [33] & 98.1 & 92.5 & 97 \\ \hline Kernel sparse coding scheme [34] & 100 & 91.98 & 93.50 \\ \hline SVM [35] & 92.50 & 91.30 & 91.90 \\ \hline DCNN [37] & 93 & 95 & 95 \\ \hline ANN/CNN [38] & 98.30 & 96.72 & 97.50 \\ \hline MobileNetV2 [39] & 33 & 92 & 72 \\ \hline Multilevel Neural Network [40] & 100 & 96.87 & 97.5 \\ \hline SVM (Proposed method) & 97.8 & 99.14 & 98 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance comparison among some most recent procedures indicated in literature that used the PH2 database.  
In Figure 8, the boxplot of the obtained performance is plotted for a given number of discarded low-quality images. It is clear that even after discarding just a few images, the system performance changes significantly, and high performance can be obtained with a selective choice of images.  
Figure 8 highlights the great impact of image quality on CAD performance. Therefore, care should be taken in the construction of the database during the image acquisition phase, requiring optimal focusing, good and fixed contrast, and avoiding reflections and color changes as a consequence of different color temperatures of light used in the acquisition phase.  
Figure 8: Performance of the CAD system for different ND values.  
Figure 7: The box plots of _TN_, _TP_, _FN_, and _FP_ statistics for PH2 database with ND = 20.