Background:
Skin cancer is one of the most common forms of cancers among
humans. It can be classified as non-melanoma and melanoma. Although melanomas are less
common than non-melanomas, the former is the most common cause of mortality. Therefore,
it becomes necessary to develop a Computer-aided Diagnosis (CAD) aiming to detect
this kind of lesion and enable the diagnosis of the disease at an early stage in order to augment
the patient’s survival likelihood.
Aims:
This paper aims to develop a simple method capable of detecting and classifying skin
lesions using dermoscopy images based on ABCD rules.
Methods:
The proposed approach follows four steps. 1) The preprocessing stage consists of
filtering and contrast enhancing algorithms. 2) The segmentation stage aims at detecting the
lesion. 3) The feature extraction stage based on the calculation of the four parameters which
are asymmetry, border irregularity, color and diameter. 4) The classification stage based on
the summation of the four extracted parameters multiplied by their weights yields the total
dermoscopy value (TDV); hence, the lesion is classified into benign, suspicious or malignant.
The proposed approach is implemented in the MATLAB environment and the experiment
is based on PH2 database containing suspicious melanoma skin cancer.
Results and Conclusion:
Based on the experiment, the accuracy of the developed approach
is 90%, which reflects its reliability.