Stack auto-encoders approach for malignant melanoma diagnosis in dermoscopy images

In this paper we present Computer Aided Diagnosis (CAD) system for malignant melanoma diagnostic based on deep learning using Stack Auto-Encoders. The dermoscopy images are taken from Dermatology Information System (DermIS) and DermQuest, the image enhancement is achieved by various pre-processing approaches. The extracted features are based on Discrete Wavelet Transform (DWT) and texture Analysis. These features become the input to Stack Auto-Encoders (SAEs) for training and testing the lesions as malignant or benign. The experimental results show the rate of accuracy for texture analysis and SAEs is 89.3%, while using DWT and SAEs gives a higher rate of accuracy about 94%. The experimental results prove that the proposed approaches are more accurate than other approaches in this field of melanoma diagnosis.