Malignant melanoma detection using multi layer preceptron with visually imperceptible features and PCA components from MED-NODE dataset

In this paper, a scheme is worked out for classification of images belonging to malignant melanoma and nevus class by multi layer neural network architecture with different trainings and cost functions. Total 1,875 shape, colour and texture features are extracted from 170 images from MED-NODE dataset. With the total 1,875 features an accuracy of 82.05% is achieved. Feature ranking algorithm ReliefF is used for ranking these features. MLP is run with varying number of best ranked features. With 10 best features an accuracy of 83.33%, sensitivity of 86.77% and specificity of 72.78% are achieved with 3 fold cross-validation. Effect of pre-processing the features with principal component analysis is explored and found that the optimal number of principal components is 25, which yields a maximum accuracy of 87.18% which is much higher than the previously reported accuracy level with this dataset.