Authors:
Dmitriy Dubovitskiy
1
;
Vladimir Devyatkov
2
and
Glenn Richer
3
Affiliations:
1
Oxford Recognition Ltd, United Kingdom
;
2
Bauman Moscow State Technical University, Russian Federation
;
3
Partner and Rising Curve LLP, United Kingdom
Keyword(s):
Skin Cancer, Mobile Device, Pattern Analysis, Decision Making, Object Recognition, Image Morphology, Machine Vision, Computational Geometry.
Related
Ontology
Subjects/Areas/Topics:
Biomedical Engineering
;
Biomedical Equipment
;
Biomedical Instrumentation
;
Biomedical Instruments and Devices
;
Computer-Aided Detection and Diagnosis
;
Emerging Technologies
;
Imaging and Visualization Devices
;
Telecommunications
;
Wireless and Mobile Technologies
;
Wireless Information Networks and Systems
Abstract:
Robotic systems and autonomous decision making systems are increasingly becoming a significant part of
our everyday routines. Object recognition is an area of computer science in which automated algorithms
work behind a graphical user interface or similar vehicle for interaction with users or some other feature
of the external world. From a user perspective this interaction with the underlying algorithm may not be
immediately apparent. This paper presents an outline of a particular form of image interpretation via mobile
devices as a method of skin cancer screening. The use of mobile hardware resources is intrinsically interconnected
with the decision making engine built into the processing system. The challenging fundamental
problem of computational geometry is in offering a software - hardware solution for image recognition in
a complex environment where not all aspects of that environment can fully be captured for use within the
algorithm. The unique combination of hardware - soft
ware interaction described in this paper brings image
processing within such an environment to the point where accurate and stable operation is possible, offering
a higher level of flexibility and automation. The Fuzzy logic classification method makes use of a set of
features which include fractal parameters derived from generally understood Fractal Theory. The automated
learning system is helping to develop the system into one capable of near-autonomous operation. The methods
discussed potentially have a wide range of applications in ‘machine vision’. However, in this publication, we
focus on the development and implementation of a skin cancer screening system that can be used by nonexperts
so that in cases where cancer is suspected a patient can immediately be referred to an appropriate
specialist.
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