Melanoma is a fatal form of skin cancer when left undiagnosed. Computer-aided 
diagnosis systems powered by convolutional neural networks (CNNs) can improve 
diagnostic accuracy and save lives. CNNs have been successfully used in both 
skin lesion segmentation and classification. For reasons heretofore unclear, 
previous works have found image segmentation to be, conflictingly, both 
detrimental and beneficial to skin lesion classification. We investigate the 
effect of expanding the segmentation border to include pixels surrounding the 
target lesion. Ostensibly, segmenting a target skin lesion will remove 
inessential information, non-lesion skin, and artifacts to aid in 
classification. Our results indicate that segmentation border enlargement 
produces, to a certain degree, better results across all metrics of interest 
when using a convolutional based classifier built using the transfer learning 
paradigm. Consequently, preprocessing methods which produce borders larger than 
the actual lesion can potentially improve classifier performance, more than both 
perfect segmentation, using dermatologist created ground truth masks, and no 
segmentation altogether.