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.
Dicussion: This paper aims to develop a simple method capable of detecting and classifying
skin lesions using dermoscopy images based on ABCD rules. 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.
Conclusion: Based on the experiment, the accuracy of the developed approach is 90%,
which reflects its reliability.