Early detection of Melanoma (skin cancer) and its classes (Malignant, Atypical, Common Nevus) is always beneficial for patients. Till now researchers have designed many Computer Aided Detection (CAD) systems which have focused on providing binary results (i.e. either presence or absence of any class of melanoma). As these systems do not provide relative extent of lesions belonging to each class, they usually lack decision support for dermatologists (in case of suspiciousness of a lesion) and complete reliability for routine clinical use. To overcome these problems, a two stage framework is proposed incorporating a new fuzzy membership function based on Lagrange Interpolation Curve Fitting method. This framework returns analogue values for a lesion which represents its relative extent in a particular class (helpful in recreating suspiciousness), hence having a greater degree of acceptability among dermatologists. A two stage CAD framework proposed here uses dermoscopic image dataset as input. In the first stage pre-processing, segmentation and feature extraction is performed while in the next stage fuzzy membership values for the three classes are calculated using Gaussian, Bell and the proposed membership function. A comparative study is done on the basis of sensitivity and specificity for the three membership functions.