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Skin cancer is a widespread, global, and potentially deadly disease, whichover the last three decades has afflicted more lives in the USA than all otherforms of cancer combined. There have been a lot of promising recent worksutilizing deep network architectures, such as FCNs, U-Nets, and ResNets, fordeveloping automated skin lesion segmentation. This paper investigates variouspre- and post-processing techniques for improving the performance of U-Nets asmeasured by the Jaccard Index. The dataset provided as part of the "2017 ISBIChallenges on Skin Lesion Analysis Towards Melanoma Detection" was used forthis evaluation and the performance of the finalist competitors was thestandard for comparison. The pre-processing techniques employed in the proposedsystem included contrast enhancement, artifact removal, and vignettecorrection. More advanced image transformations, such as local binary patternsand wavelet decomposition, were also employed to augment the raw grayscaleimages used as network input features. While the performance of the proposedsystem fell short of the winners of the challenge, it was determined that usingwavelet decomposition as an early transformation step improved the overallperformance of the system over pre- and post-processing steps alone.

The Effects of Image Pre- and Post-Processing, Wavelet Decomposition, and Local Binary Patterns on U-Nets for Skin Lesion Segmentation