Authors:
Athanasios Kallipolitis
and
Ilias Maglogiannis
Affiliation:
Department of Digital Systems, University if Piraeus, Piraeus, Greece 21st Department of Dermatology, Andreas Syggros Hospital, Medical School, National and Kapodistrian University of Athens, Athens, Greece
Keyword(s):
Reflectance Confocal Microscopy, Bag of Visual Words, Skin Cancer, Neural Networks, Speeded up Robust Features, Haralick.
Abstract:
Reflectance Confocal Microscopy (RCM) is an ancillary, non-invasive method for reviewing horizontal sections from areas of interest of the skin at a high resolution. In this paper, we propose a method based on the exploitation of Bag of Visual Words (BOVW) technique, coupled with a plain neural network to classify extracted information into discrete patterns of skin cancer types. The paper discusses the technical details of implementation, while providing promising initial results that reach 90% accuracy. Automated classification of RCM images can lead to the establishment of a reliable procedure for the assessment of skin cancer cases and the training of medical personnel through the quantization of image content. Moreover, early detected benign tumours can reduce significantly the number of time and resource consuming biopsies.