# A Dermoscopic Inspired System for Localization and Malignancy Classification of Melanocytic Lesions
## 3 Results
### Evaluation of Features Extracted and Lesion Classification: Results

Table 2: _Cont._  
Figure 11: Plot of features extracted versus the \(p\)-values: (**A**) lesion specific features, (**B**) statistical color features (CRG, CGB, CBR, CGK, and CBK indicate correlation between red (R), green (G), blue (B), gray values (K)), V indicates color variance, and E indicates entropy). preceding steps. The hair detection algorithm considers the dermoscopic knowledge of hair shafts thereby neglecting a overlap of the attributes of lesion and hair. Such an algorithm prevents loss of lesion specific information and efficiently eliminates the light and dark hair that subsequently aids in improving the lesion segmentation accuracy. Border irregularity is a major indication of malignancy of melanocytic lesions. Thus, while localizing the ROI appropriate care has to be taken to prevent the loss of lesion border details. Geometric deformable models incorporating color information have provided promising segmentation accuracy even in the presence of background noise and poor contrast. The segmentation process was followed by extraction of a set of 48 features specific to shape, color, texture and pigment network from the segmented ROI's to facilitate identification of benign and malignant lesions. The non-parametric Wilcoxon Rank Sum statistics was used to obtain the \(p\)-values for the features extracted with the goal of finding the best features. Of late, deep learning techniques have been extensively used in skin lesion classification [12; 13; 31]. In spite of the fact that these architectures have increase the accuracy of classification using large data for learning, the optimization of network parameters for reducing computational complexity is unexplored. A quantitative comparison of the proposed method and the state of art methods reported above may be tenuous due to the diversity of the datasets involved, however a comparative analysis of the studies carried out using the same datasets is given in Table 6. Sensitivity indicates the accurate rate of classification of melanoma lesions. Specificity indicates the accurate rate of classification of benign lesions, whereas accuracy gives a cumulative score of classification of benign and malignant lesions. In [5], the sensitivity was higher in contrast to specificity, since the main focus was on color feature of the lesions. An imbalance in sensitivity and specificity was obtained by [PERSON] et al. [31], by employing a deep learning-based architecture. A methodological approach to detect pigmented skin lesions was proposed in [32]. [PERSON] et al. [33], have used standard color, shape and texture feature for classification after applying Delaunay Tri-angulation based segmentation acc Nonetheless, the comparison provides us with relevant information about the significance of the proposed method. Nonetheless, the proposed method employs domain specific features, thereby improving the accuracy in classification of benign and malignant lesions. However, the study did not consider the thickness feature of the lesion due to lack of third dimensional image data and ground truth. The thickness feature would be an important parameter to rate the stage of malignancy once, the lesion malignancy has been detected by the classification model. Another limitation of the study if the processing time, since it approximately takes 90 s, on an average system of 8 GB RAM, and clock frequency of 1.60 GHz to provide the diagnosis once, the dermoscopic image is given as the input to the system The trained system can be employed in a clinical scenario, by using a dermoscopic based image capturing system, since a dermo scope would enhance the resolution of the lesions that would aid in better analysis, rather than a normal image capturing device.