Τίτλος:
Fully Connected Visual Words for the Classification of Skin Cancer
Confocal Images
Γλώσσες Τεκμηρίου:
Αγγλικά
Περίληψη:
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.
Συγγραφείς:
Kallipolitis, Athanasios
Stratigos, Alexandros
Zarras, Alexios
and Maglogiannis, Ilias
Τίτλος συνεδρίου:
PROCEEDINGS OF THE 15TH INTERNATIONAL JOINT CONFERENCE ON COMPUTER
VISION, IMAGING AND COMPUTER GRAPHICS THEORY AND APPLICATIONS, VOL 5:
VISAPP
Λέξεις-κλειδιά:
Reflectance Confocal Microscopy; Bag of Visual Words; Skin Cancer;
Neural Networks; Speeded up Robust Features; Haralick
DOI:
10.5220/0009328808530858
