Rethinking Skin Lesion Segmentation in a Convolutional Classifier

Abstract

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.

Keywords: Convolutional neural networks; Deep learning; Machine learning; Medical decision support systems; Medical image analysis; Skin lesions.

Fig. 1

Sample input images

Fig. 2

Input images: a perfectly segmented image (a), progressively larger imperfectly segmented images (obtained with dilation using disk structuring elements with radii 25 (b), 50 (c), 75 (d), and 100 (e) pixels, and an unsegmented image (f)

Fig. 3

Confusion matrix example images from unsegmented inputs classified on the VGG-16 classifier