Computer-aided detection of melanoma a case study
Date Issued
2018
Author(s)
Trajkova, Vesna
Abstract
Melanoma is the most dangerous form of skin
cancer, and its detection at an early stage can allow timely
treatment and prevention of fatal consequences. In this paper we
present a case study of computer-aided diagnostics of melanoma
using images of patients moles. The initial study was performed
on two datasets: a benchmark dataset which is publicly available
and a second one, containing images that were taken in hospitals
in Macedonia. We present the obtained results and a short
discussion of further directions for research. The results on
the initial dataset were promising and showed 83% accuracy
in the detection of the melanoma on the benchmark dataset.
However, the same approach applied on the Macedonian dataset,
the results could not be reproduced due to the low number of
positive examples. The results showed that the performance of the
classifiers did not benefit from under-sampling or oversampling
techniques, nor did from feature selection. We can conclude that
to build a reliable system for melanoma detection, a datasets of
hundreds of images is not enough to train a machine-learning
based model.
cancer, and its detection at an early stage can allow timely
treatment and prevention of fatal consequences. In this paper we
present a case study of computer-aided diagnostics of melanoma
using images of patients moles. The initial study was performed
on two datasets: a benchmark dataset which is publicly available
and a second one, containing images that were taken in hospitals
in Macedonia. We present the obtained results and a short
discussion of further directions for research. The results on
the initial dataset were promising and showed 83% accuracy
in the detection of the melanoma on the benchmark dataset.
However, the same approach applied on the Macedonian dataset,
the results could not be reproduced due to the low number of
positive examples. The results showed that the performance of the
classifiers did not benefit from under-sampling or oversampling
techniques, nor did from feature selection. We can conclude that
to build a reliable system for melanoma detection, a datasets of
hundreds of images is not enough to train a machine-learning
based model.
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