1. A method of constructing a classifier for melanoma detection comprising: constructing a codebook of representative features based on a plurality of target-disease-irrelevant images; extracting a plurality of transfer-learned target-disease features from a plurality of target-disease images according to the codebook, wherein the step of extracting the plurality of transfer-learned target-disease features comprises: segmenting a target region from each of the target-disease images to correspondingly generate a segmented target-disease image; and extracting the transfer-learned target-disease features from the segmented target-disease images; and performing supervised learning based on the transfer-learned target-disease features to train the classifier for melanoma detection, wherein the step of segmenting the target region from each of the target-disease images to correspondingly generate the segmented target-disease image comprises: for each of the target-disease images: initializing a contour at a center of the target region according to color; evolving the contour such that a predefined energy function is minimized so as to obtain a terminated boundary; and segmenting the target region from the target-disease image according to the terminated boundary to generate the segmented target-disease image.