4. Experimental results and discussion

#### 4.1 Hardware and software specifications

The experiments are executed on processor Intel, core i3-2330 M @ 2.20 GHz and RAM 4 GB. The system type is windows 7 ultimate of 64-bit operating and the software used for research implementation is MATLAB R2013a.

#### 4.2 Dataset

The performance of our research is tested on PH<sup>2</sup> dataset [32]. It consists of 200 8-bit RGB dermoscopic images of melanocytic lesions with a resolution of 768 � 560 pixels. This image database contains 80 common nevi, 80 atypical nevi, and 40 melanomas. The dermoscopic images were obtained at the Dermatology Service of Hospital Pedro Hispano (Matosinhos, Portugal) under the same conditions through tuebinger mole analyzer system using a magnification of 20 times.

#### 4.3 Implementation of ABCD rule

The ABCD rule is implemented on the PH2 dataset and a random selection of segmentation and classification of successful results are presented in Figure 13. For each image, the segmented lesion is surrounded by a solid blue line, and the calculated value of the TDS and the classification result are presented in the bottom-left corner.

#### 4.4 Discussion

The results of this research are compared with the results of [31], in terms of accuracy, sensitivity, and specificity. The running time for the diagnosis process of 200 8-bit RGB images is 1670 s, or an average of 8.35 s per each examined lesion.

The performance of the proposed work is evaluated by one of the well-known metrics called the confusion matrix as described in Table 2. It presents the correct and wrong classification rates that resulted from the implementation of the ABCD rule on PH2 dataset. This image database contains 80 common nevi, 80 atypical nevi, and 40 melanomas.

Table 3 summarizes the calculated values of true positive (TP), false negative (FN), false positive (FP), and true negative (TN) of the three classes, benign (B), suspicious (S), and high suspicious (H).

The accuracy, sensitivity, and specificity formulas are described in the following equations, respectively. Table 4 summarizes the achieved performance of the three classes.

Diagnosis of Skin Lesions Based on Dermoscopic Images Using Image Processing Techniques DOI: http://dx.doi.org/10.5772/intechopen.88065

Figure 13. Results sample.


#### Table 2.

The ABCD rule performance confusion matrix.


#### Table 3.

Summary of correct and wrong classifications.


#### Table 4.

Benchmarking results of the proposed work applied to the PH2 database.

$$Accuracy = \frac{TP + TN}{TP + FP + FN + TN} \tag{17}$$

$$Sensitivity = \frac{TP}{TP + FN} \tag{18}$$

$$Specificity = \frac{TN}{TN + FP} \tag{19}$$
