Malignant Melanoma Diagnosis Using Intelligence Approaches

Malignant melanoma is one of the major deadliest of skin cancers. Medical Informatics employs the computer technology for diagnostic such as Computer Aided Diagnosis (CAD). Many of researchers have developed CAD systems for melanoma diagnostic. The early diagnosis is leaded to reduce the melanoma-related deaths. This paper presents intelligence approaches namely, Artificial Neural Network (ANN), Adaptive-Network-based Fuzzy Inference System (ANFIS) and Support Vector Machine (SVM). The dermoscopy images are taken from Dermatology Information System (DermIS) and DermQuest, image enhancement is achieved by various pre-processing approaches. The extracted features are based on Discrete Wavelet Transform (DWT), and Principle Component Analysis (PCA) is used to take the eigenvalue as features. These features become the input to the various classification approaches such as ANN, ANFIS and SVM to classify the lesions as malignant or benign. The results show the rate of accuracy for ANFIS and SVM is 95.2%, while ANN gives higher rate of accuracy about 98.8%. Moreover; the comparative results are indicated that the proposed approaches have excellent accuracy than other approaches in this field of melanoma diagnosis.